paxtools.bib

% This file was created with JabRef 2.7b.
% Encoding: UTF-8

@ARTICLE{Aerts2006,
  author = {Aerts, Stein and Lambrechts, Diether and Maity, Sunit and {Van Loo},
	Peter and Coessens, Bert and {De Smet}, Frederik and Tranchevent,
	Leon-Charles and {De Moor}, Bart and Marynen, Peter and Hassan, Bassem
	and Carmeliet, Peter and Moreau, Yves},
  title = {{Gene prioritization through genomic data fusion.}},
  journal = {Nature biotechnology},
  year = {2006},
  volume = {24},
  pages = {537--44},
  number = {5},
  month = may,
  abstract = {The identification of genes involved in health and disease remains
	a challenge. We describe a bioinformatics approach, together with
	a freely accessible, interactive and flexible software termed Endeavour,
	to prioritize candidate genes underlying biological processes or
	diseases, based on their similarity to known genes involved in these
	phenomena. Unlike previous approaches, ours generates distinct prioritizations
	for multiple heterogeneous data sources, which are then integrated,
	or fused, into a global ranking using order statistics. In addition,
	it offers the flexibility of including additional data sources. Validation
	of our approach revealed it was able to efficiently prioritize 627
	genes in disease data sets and 76 genes in biological pathway sets,
	identify candidates of 16 mono- or polygenic diseases, and discover
	regulatory genes of myeloid differentiation. Furthermore, the approach
	identified a novel gene involved in craniofacial development from
	a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like
	birth defects. The approach described here offers an alternative
	integrative method for gene discovery.},
  doi = {10.1038/nbt1203},
  issn = {1087-0156},
  keywords = {Algorithms,Animals,Cell Differentiation,Chromosome Mapping,Computational
	Biology,Computational Biology: methods,Gene Expression Regulation,Genetic
	Predisposition to Disease,Humans,Models, Genetic,Models, Statistical,ROC
	Curve,Sensitivity and Specificity,Software,Zebrafish},
  pmid = {16680138},
  publisher = {Nature Publishing Group},
  shorttitle = {Nat Biotech},
  url = {http://dx.doi.org/10.1038/nbt1203}
}

@ARTICLE{babur2010chibe,
  author = {Babur, Ozgun and Dogrusoz, Ugur and Demir, Emek and Sander, Chris},
  title = {{ChiBE: interactive visualization and manipulation of BioPAX pathway
	models.}},
  journal = {Bioinformatics (Oxford, England)},
  year = {2010},
  volume = {26},
  pages = {429--31},
  number = {3},
  month = feb,
  abstract = {Representing models of cellular processes or pathways in a graphically
	rich form facilitates interpretation of biological observations and
	generation of new hypotheses. Solving biological problems using large
	pathway datasets requires software that can combine data mapping,
	querying and visualization as well as providing access to diverse
	data resources on the Internet. ChiBE is an open source software
	application that features user-friendly multi-view display, navigation
	and manipulation of pathway models in BioPAX format. Pathway views
	are rendered in a feature-rich format, and may be laid out and edited
	with state-of-the-art visualization methods, including compound or
	nested structures for visualizing cellular compartments and molecular
	complexes. Users can easily query and visualize pathways through
	an integrated Pathway Commons query tool and analyze molecular profiles
	in pathway context.},
  doi = {10.1093/bioinformatics/btp665},
  issn = {1367-4811},
  keywords = {Biological,Computational Biology,Computational Biology: methods,Computer
	Graphics,Databases,Factual,Information Storage and Retrieval,Internet,Models,Signal
	Transduction,Software,User-Computer Interface},
  pmid = {20007251},
  publisher = {Oxford Univ Press},
  url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2815657\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Bader2006,
  author = {Bader, Gary D and Cary, Michael P and Sander, Chris},
  title = {{Pathguide: a pathway resource list.}},
  journal = {Nucleic acids research},
  year = {2006},
  volume = {34},
  pages = {D504--6},
  number = {Database issue},
  month = jan,
  abstract = {Pathguide: the Pathway Resource List (http://pathguide.org) is a meta-database
	that provides an overview of more than 190 web-accessible biological
	pathway and network databases. These include databases on metabolic
	pathways, signaling pathways, transcription factor targets, gene
	regulatory networks, genetic interactions, protein-compound interactions,
	and protein-protein interactions. The listed databases are maintained
	by diverse groups in different locations and the information in them
	is derived either from the scientific literature or from systematic
	experiments. Pathguide is useful as a starting point for biological
	pathway analysis and for content aggregation in integrated biological
	information systems.},
  address = {Computational Biology Center, Memorial Sloan-Kettering Cancer Center,
	1275 York Avenue, Box 460, New York, NY 10021, USA.},
  doi = {10.1093/nar/gkj126},
  isbn = {1362-4962},
  issn = {1362-4962},
  keywords = {Databases,Gene Expression Regulation,Genetic,Genetic: standards,Internet,Metabolism,Proteins,Proteins:
	chemistry,Proteins: metabolism,Signal Transduction,Systems Integration,Transcription
	Factors,Transcription Factors: metabolism,User-Computer Interface},
  pmid = {16381921},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/16381921 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1347488\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Caspi2010,
  author = {Caspi, Ron and Altman, Tomer and Dale, Joseph M and Dreher, Kate
	and Fulcher, Carol A and Gilham, Fred and Kaipa, Pallavi and Karthikeyan,
	Athikkattuvalasu S and Kothari, Anamika and Krummenacker, Markus
	and Latendresse, Mario and Mueller, Lukas A and Paley, Suzanne and
	Popescu, Liviu and Pujar, Anuradha and Shearer, Alexander G and Zhang,
	Peifen and Karp, Peter D},
  title = {{The MetaCyc database of metabolic pathways and enzymes and the BioCyc
	collection of pathway/genome databases.}},
  journal = {Nucleic acids research},
  year = {2010},
  volume = {38},
  pages = {D473--9},
  number = {Database issue},
  month = jan,
  abstract = {The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible
	resource for metabolic pathways and enzymes from all domains of life.
	The pathways in MetaCyc are experimentally determined, small-molecule
	metabolic pathways and are curated from the primary scientific literature.
	With more than 1400 pathways, MetaCyc is the largest collection of
	metabolic pathways currently available. Pathways reactions are linked
	to one or more well-characterized enzymes, and both pathways and
	enzymes are annotated with reviews, evidence codes, and literature
	citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific
	Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full
	genome and predicted metabolic network of one organism. The network,
	which is predicted by the Pathway Tools software using MetaCyc as
	a reference, consists of metabolites, enzymes, reactions and metabolic
	pathways. BioCyc PGDBs also contain additional features, such as
	predicted operons, transport systems, and pathway hole-fillers. The
	BioCyc Web site offers several tools for the analysis of the PGDBs,
	including Omics Viewers that enable visualization of omics datasets
	on two different genome-scale diagrams and tools for comparative
	analysis. The BioCyc PGDBs generated by SRI are offered for adoption
	by any party interested in curation of metabolic, regulatory, and
	genome-related information about an organism.},
  address = {SRI International, 333 Ravenswood, Menlo Park, CA 94025, USA.},
  doi = {10.1093/nar/gkp875},
  isbn = {1362-4962},
  issn = {1362-4962},
  keywords = {Animals,Archaeal,Bacterial,Biological,Computational Biology,Computational
	Biology: methods,Computational Biology: trends,Databases,Genetic,Genome,Humans,Information
	Storage and Retrieval,Information Storage and Retrieval: methods,Internet,Models,Nucleic
	Acid,Plant,Protein,Protein Structure,Software,Tertiary,Viral},
  pmid = {19850718},
  url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2808959\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Cerami2012,
  author = {Cerami, Ethan and Gao, Jianjiong and Dogrusoz, Ugur and Gross, Benjamin
	E and Sumer, Selcuk Onur and Aksoy, B\"{u}lent Arman and Jacobsen,
	Anders and Byrne, Caitlin J and Heuer, Michael L and Larsson, Erik
	and Antipin, Yevgeniy and Reva, Boris and Goldberg, Arthur P and
	Sander, Chris and Schultz, Nikolaus},
  title = {{The cBio cancer genomics portal: an open platform for exploring
	multidimensional cancer genomics data.}},
  journal = {Cancer discovery},
  year = {2012},
  volume = {2},
  pages = {401--4},
  number = {5},
  month = may,
  abstract = {The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access
	resource for interactive exploration of multidimensional cancer genomics
	data sets, currently providing access to data from more than 5,000
	tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal
	significantly lowers the barriers between complex genomic data and
	cancer researchers who want rapid, intuitive, and high-quality access
	to molecular profiles and clinical attributes from large-scale cancer
	genomics projects and empowers researchers to translate these rich
	data sets into biologic insights and clinical applications.},
  doi = {10.1158/2159-8290.CD-12-0095},
  issn = {2159-8290},
  keywords = {Database Management Systems,Databases,Factual,Genomics,Humans,Internet,Neoplasms,Neoplasms:
	genetics},
  pmid = {22588877},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/22588877}
}

@ARTICLE{Cerami2006,
  author = {Cerami, Ethan G and Bader, Gary D and Gross, Benjamin E and Sander,
	Chris},
  title = {{cPath: open source software for collecting, storing, and querying
	biological pathways.}},
  journal = {BMC bioinformatics},
  year = {2006},
  volume = {7},
  pages = {497},
  month = jan,
  abstract = {Biological pathways, including metabolic pathways, protein interaction
	networks, signal transduction pathways, and gene regulatory networks,
	are currently represented in over 220 diverse databases. These data
	are crucial for the study of specific biological processes, including
	human diseases. Standard exchange formats for pathway information,
	such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection
	of this data for biological research, but mechanisms for common storage
	and communication are required.},
  address = {Computational Biology Center, Memorial Sloan-Kettering Cancer Center
	1275 York Avenue, Box 460, New York, NY 10021, USA. cpath-bmc@cbio.mskcc.org},
  doi = {10.1186/1471-2105-7-497},
  isbn = {1471-2105},
  issn = {1471-2105},
  keywords = {Computational Biology,Computational Biology: methods,Computer Graphics,Databases,Factual,Humans,Information
	Storage and Retrieval,Internet,Programming Languages,Signal Transduction,Software,Software
	Design,Systems Integration,User-Computer Interface},
  pmid = {17101041},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/17101041 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1660554\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Cerami2011,
  author = {Cerami, Ethan G and Gross, Benjamin E and Demir, Emek and Rodchenkov,
	Igor and Babur, Ozg\"{u}n and Anwar, Nadia and Schultz, Nikolaus
	and Bader, Gary D and Sander, Chris},
  title = {{Pathway Commons, a web resource for biological pathway data}},
  journal = {Nucleic Acids Res},
  year = {2011},
  volume = {39},
  pages = {D685--90},
  number = {Database issue},
  month = jan,
  abstract = {Pathway Commons (http://www.pathwaycommons.org) is a collection of
	publicly available pathway data from multiple organisms. Pathway
	Commons provides a web-based interface that enables biologists to
	browse and search a comprehensive collection of pathways from multiple
	sources represented in a common language, a download site that provides
	integrated bulk sets of pathway information in standard or convenient
	formats and a web service that software developers can use to conveniently
	query and access all data. Database providers can share their pathway
	data via a common repository. Pathways include biochemical reactions,
	complex assembly, transport and catalysis events and physical interactions
	involving proteins, DNA, RNA, small molecules and complexes. Pathway
	Commons aims to collect and integrate all public pathway data available
	in standard formats. Pathway Commons currently contains data from
	nine databases with over 1400 pathways and 687,000 interactions and
	will be continually expanded and updated.},
  address = {Computational Biology Center, Memorial Sloan-Kettering Cancer Center
	1275 York Avenue, Box 460, New York, NY 10065, USA.},
  doi = {10.1093/nar/gkq1039},
  isbn = {1362-4962},
  issn = {1362-4962},
  keywords = {Biological,Databases,Disease,Disease: classification,Factual,Genetic,Genomics,Internet,Models,Protein,Systems
	Integration,User-Computer Interface},
  pmid = {21071392},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/21071392 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3013659\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Chen2009,
  author = {Chen, Jing and Bardes, Eric E and Aronow, Bruce J and Jegga, Anil
	G},
  title = {{ToppGene Suite for gene list enrichment analysis and candidate gene
	prioritization.}},
  journal = {Nucleic acids research},
  year = {2009},
  volume = {37},
  pages = {W305--11},
  number = {Web Server issue},
  month = jul,
  abstract = {ToppGene Suite (http://toppgene.cchmc.org; this web site is free and
	open to all users and does not require a login to access) is a one-stop
	portal for (i) gene list functional enrichment, (ii) candidate gene
	prioritization using either functional annotations or network analysis
	and (iii) identification and prioritization of novel disease candidate
	genes in the interactome. Functional annotation-based disease candidate
	gene prioritization uses a fuzzy-based similarity measure to compute
	the similarity between any two genes based on semantic annotations.
	The similarity scores from individual features are combined into
	an overall score using statistical meta-analysis. A P-value of each
	annotation of a test gene is derived by random sampling of the whole
	genome. The protein-protein interaction network (PPIN)-based disease
	candidate gene prioritization uses social and Web networks analysis
	algorithms (extended versions of the PageRank and HITS algorithms,
	and the K-Step Markov method). We demonstrate the utility of ToppGene
	Suite using 20 recently reported GWAS-based gene-disease associations
	(including novel disease genes) representing five diseases. ToppGene
	ranked 19 of 20 (95\%) candidate genes within the top 20\%, while
	ToppNet ranked 12 of 16 (75\%) candidate genes among the top 20\%.},
  doi = {10.1093/nar/gkp427},
  file = {:home/emek/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen et al. - 2009 - ToppGene Suite for gene list enrichment analysis and candidate gene prioritization.pdf:pdf},
  issn = {1362-4962},
  keywords = {Animals,Disease,Disease: genetics,Genes,Humans,Internet,Mice,Protein
	Interaction Mapping,Proteins,Proteins: genetics,Software},
  pmid = {19465376},
  url = {http://nar.oxfordjournals.org/cgi/content/abstract/37/suppl\_2/W305}
}

@ARTICLE{Chuang2007,
  author = {Chuang, Han-Yu and Lee, Eunjung and Liu, Yu-Tsueng and Lee, Doheon
	and Ideker, Trey},
  title = {{Network-based classification of breast cancer metastasis.}},
  journal = {Molecular systems biology},
  year = {2007},
  volume = {3},
  pages = {140},
  month = jan,
  abstract = {Mapping the pathways that give rise to metastasis is one of the key
	challenges of breast cancer research. Recently, several large-scale
	studies have shed light on this problem through analysis of gene
	expression profiles to identify markers correlated with metastasis.
	Here, we apply a protein-network-based approach that identifies markers
	not as individual genes but as subnetworks extracted from protein
	interaction databases. The resulting subnetworks provide novel hypotheses
	for pathways involved in tumor progression. Although genes with known
	breast cancer mutations are typically not detected through analysis
	of differential expression, they play a central role in the protein
	network by interconnecting many differentially expressed genes. We
	find that the subnetwork markers are more reproducible than individual
	marker genes selected without network information, and that they
	achieve higher accuracy in the classification of metastatic versus
	non-metastatic tumors.},
  address = {Bioinformatics Program, University of California San Diego, La Jolla,
	CA 92093, USA.},
  doi = {10.1038/msb4100180},
  isbn = {1744-4292},
  issn = {1744-4292},
  keywords = {Biological,Biological: analysis,Breast Neoplasms,Breast Neoplasms:
	genetics,Breast Neoplasms: pathology,Computational Biology,Female,Gene
	Expression Profiling,Gene Expression Regulation,Humans,Neoplasm Metastasis,Neoplasm
	Metastasis: genetics,Neoplasm Metastasis: pathology,Neoplasm Proteins,Neoplasm
	Proteins: genetics,Neoplastic,Oligonucleotide Array Sequence Analysis,Tumor
	Markers},
  pmid = {17940530},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/17940530 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2063581\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Ciriello2012,
  author = {Ciriello, Giovanni and Cerami, Ethan and Sander, Chris and Schultz,
	Nikolaus},
  title = {{Mutual exclusivity analysis identifies oncogenic network modules.}},
  journal = {Genome research},
  year = {2012},
  volume = {22},
  pages = {398--406},
  number = {2},
  month = feb,
  abstract = {Although individual tumors of the same clinical type have surprisingly
	diverse genomic alterations, these events tend to occur in a limited
	number of pathways, and alterations that affect the same pathway
	tend to not co-occur in the same patient. While pathway analysis
	has been a powerful tool in cancer genomics, our knowledge of oncogenic
	pathway modules is incomplete. To systematically identify such modules,
	we have developed a novel method, Mutual Exclusivity Modules in cancer
	(MEMo). The method uses correlation analysis and statistical tests
	to identify network modules by three criteria: (1) Member genes are
	recurrently altered across a set of tumor samples; (2) member genes
	are known to or are likely to participate in the same biological
	process; and (3) alteration events within the modules are mutually
	exclusive. Applied to data from the Cancer Genome Atlas (TCGA), the
	method identifies the principal known altered modules in glioblastoma
	(GBM) and highlights the striking mutual exclusivity of genomic alterations
	in the PI(3)K, p53, and Rb pathways. In serous ovarian cancer, we
	make the novel observation that inactivation of BRCA1 and BRCA2 is
	mutually exclusive of amplification of CCNE1 and inactivation of
	RB1, suggesting distinct alternative causes of genomic instability
	in this cancer type; and, we identify RBBP8 as a candidate oncogene
	involved in Rb-mediated cell cycle control. When applied to any cancer
	genomics data set, the algorithm can nominate oncogenic alterations
	that have a particularly strong selective effect and may also be
	useful in the design of therapeutic combinations in cases where mutual
	exclusivity reflects synthetic lethality.},
  doi = {10.1101/gr.125567.111},
  issn = {1549-5469},
  pmid = {21908773},
  url = {http://genome.cshlp.org/cgi/content/abstract/22/2/398}
}

@ARTICLE{Cline2007,
  author = {Cline, Melissa S and Smoot, Michael and Cerami, Ethan and Kuchinsky,
	Allan and Landys, Nerius and Workman, Chris and Christmas, Rowan
	and Avila-Campilo, Iliana and Creech, Michael and Gross, Benjamin
	and Hanspers, Kristina and Isserlin, Ruth and Kelley, Ryan and Killcoyne,
	Sarah and Lotia, Samad and Maere, Steven and Morris, John and Ono,
	Keiichiro and Pavlovic, Vuk and Pico, Alexander R and Vailaya, Aditya
	and Wang, Peng-Liang and Adler, Annette and Conklin, Bruce R and
	Hood, Leroy and Kuiper, Martin and Sander, Chris and Schmulevich,
	Ilya and Schwikowski, Benno and Warner, Guy J and Ideker, Trey and
	Bader, Gary D},
  title = {{Integration of biological networks and gene expression data using
	Cytoscape.}},
  journal = {Nature protocols},
  year = {2007},
  volume = {2},
  pages = {2366--82},
  number = {10},
  month = jan,
  abstract = {Cytoscape is a free software package for visualizing, modeling and
	analyzing molecular and genetic interaction networks. This protocol
	explains how to use Cytoscape to analyze the results of mRNA expression
	profiling, and other functional genomics and proteomics experiments,
	in the context of an interaction network obtained for genes of interest.
	Five major steps are described: (i) obtaining a gene or protein network,
	(ii) displaying the network using layout algorithms, (iii) integrating
	with gene expression and other functional attributes, (iv) identifying
	putative complexes and functional modules and (v) identifying enriched
	Gene Ontology annotations in the network. These steps provide a broad
	sample of the types of analyses performed by Cytoscape.},
  address = {Institut Pasteur, 25-28 rue du Docteur Roux, 75724 Paris cedex 15,
	France.},
  doi = {10.1038/nprot.2007.324},
  isbn = {1750-2799},
  issn = {1750-2799},
  keywords = {Computational Biology,Computational Biology: methods,Gene Expression
	Profiling,Gene Expression Profiling: methods,Gene Regulatory Networks,Genomics,Genomics:
	methods,Messenger,Messenger: metabolism,Proteomics,Proteomics: methods,RNA,Software},
  pmid = {17947979},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/17947979 http://www.ncbi.nlm.nih.gov/pubmed/17947979}
}

@ARTICLE{Croft2011,
  author = {Croft, David and O'Kelly, Gavin and Wu, Guanming and Haw, Robin and
	Gillespie, Marc and Matthews, Lisa and Caudy, Michael and Garapati,
	Phani and Gopinath, Gopal and Jassal, Bijay and Jupe, Steven and
	Kalatskaya, Irina and Mahajan, Shahana and May, Bruce and Ndegwa,
	Nelson and Schmidt, Esther and Shamovsky, Veronica and Yung, Christina
	and Birney, Ewan and Hermjakob, Henning and D'Eustachio, Peter and
	Stein, Lincoln},
  title = {{Reactome: a database of reactions, pathways and biological processes}},
  journal = {Nucleic Acids Res},
  year = {2011},
  volume = {39},
  pages = {D691--7},
  number = {Database issue},
  month = jan,
  abstract = {Reactome (http://www.reactome.org) is a collaboration among groups
	at the Ontario Institute for Cancer Research, Cold Spring Harbor
	Laboratory, New York University School of Medicine and The European
	Bioinformatics Institute, to develop an open source curated bioinformatics
	database of human pathways and reactions. Recently, we developed
	a new web site with improved tools for pathway browsing and data
	analysis. The Pathway Browser is an Systems Biology Graphical Notation
	(SBGN)-based visualization system that supports zooming, scrolling
	and event highlighting. It exploits PSIQUIC web services to overlay
	our curated pathways with molecular interaction data from the Reactome
	Functional Interaction Network and external interaction databases
	such as IntAct, BioGRID, ChEMBL, iRefIndex, MINT and STRING. Our
	Pathway and Expression Analysis tools enable ID mapping, pathway
	assignment and overrepresentation analysis of user-supplied data
	sets. To support pathway annotation and analysis in other species,
	we continue to make orthology-based inferences of pathways in non-human
	species, applying Ensembl Compara to identify orthologs of curated
	human proteins in each of 20 other species. The resulting inferred
	pathway sets can be browsed and analyzed with our Species Comparison
	tool. Collaborations are also underway to create manually curated
	data sets on the Reactome framework for chicken, Drosophila and rice.},
  address = {European Bioinformatics Institute, Wellcome Trust Genome Campus,
	Hinxton, Cambridge CB10 1SD, UK.},
  doi = {10.1093/nar/gkq1018},
  isbn = {1362-4962},
  issn = {1362-4962},
  keywords = {Biological,Biological Processes,Computer Graphics,Databases,Factual,Gene
	Expression Regulation,Genetic,Humans,Internet,Metabolic Networks
	and Pathways,Models,Protein,Signal Transduction},
  pmid = {21067998},
  url = {http://nar.oxfordjournals.org/cgi/content/abstract/39/suppl\_1/D691 http://view.ncbi.nlm.nih.gov/pubmed/21067998}
}

@ARTICLE{Czauderna2010,
  author = {Czauderna, Tobias and Klukas, Christian and Schreiber, Falk},
  title = {{Editing, validating and translating of SBGN maps.}},
  journal = {Bioinformatics (Oxford, England)},
  year = {2010},
  volume = {26},
  pages = {2340--1},
  number = {18},
  month = sep,
  abstract = {The recently proposed Systems Biology Graphical Notation (SBGN) provides
	a standard for the visual representation of biochemical and cellular
	processes. It aims to support more efficient and accurate communication
	of biological knowledge between different research communities in
	the life sciences. However, to increase the use of SBGN, tools for
	editing, validating and translating SBGN maps are desirable.},
  doi = {10.1093/bioinformatics/btq407},
  file = {:home/emek/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Czauderna, Klukas, Schreiber - 2010 - Editing, validating and translating of SBGN maps(3).pdf:pdf},
  issn = {1367-4811},
  keywords = {Computer Graphics,Software,Systems Biology,Systems Biology: methods,Systems
	Biology: standards},
  pmid = {20628075},
  url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2935428\&tool=pmcentrez\&rendertype=abstract http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/18/2340}
}

@ARTICLE{Demir2010,
  author = {Demir, Emek and Cary, Michael P and Paley, Suzanne and Fukuda, Ken
	and Lemer, Christian and Vastrik, Imre and Wu, Guanming and D'Eustachio,
	Peter and Schaefer, Carl and Luciano, Joanne and Schacherer, Frank
	and Martinez-Flores, Irma and Hu, Zhenjun and Jimenez-Jacinto, Veronica
	and Joshi-Tope, Geeta and Kandasamy, Kumaran and Lopez-Fuentes, Alejandra
	C and Mi, Huaiyu and Pichler, Elgar and Rodchenkov, Igor and Splendiani,
	Andrea and Tkachev, Sasha and Zucker, Jeremy and Gopinath, Gopal
	and Rajasimha, Harsha and Ramakrishnan, Ranjani and Shah, Imran and
	Syed, Mustafa and Anwar, Nadia and Babur, Ozg\"{u}n and Blinov, Michael
	and Brauner, Erik and Corwin, Dan and Donaldson, Sylva and Gibbons,
	Frank and Goldberg, Robert and Hornbeck, Peter and Luna, Augustin
	and Murray-Rust, Peter and Neumann, Eric and Reubenacker, Oliver
	and Samwald, Matthias and van Iersel, Martijn and Wimalaratne, Sarala
	and Allen, Keith and Braun, Burk and Whirl-Carrillo, Michelle and
	Cheung, Kei-Hoi and Dahlquist, Kam and Finney, Andrew and Gillespie,
	Marc and Glass, Elizabeth and Gong, Li and Haw, Robin and Honig,
	Michael and Hubaut, Olivier and Kane, David and Krupa, Shiva and
	Kutmon, Martina and Leonard, Julie and Marks, Debbie and Merberg,
	David and Petri, Victoria and Pico, Alex and Ravenscroft, Dean and
	Ren, Liya and Shah, Nigam and Sunshine, Margot and Tang, Rebecca
	and Whaley, Ryan and Letovksy, Stan and Buetow, Kenneth H and Rzhetsky,
	Andrey and Schachter, Vincent and Sobral, Bruno S and Dogrusoz, Ugur
	and McWeeney, Shannon and Aladjem, Mirit and Birney, Ewan and Collado-Vides,
	Julio and Goto, Susumu and Hucka, Michael and {Le Nov\`{e}re}, Nicolas
	and Maltsev, Natalia and Pandey, Akhilesh and Thomas, Paul and Wingender,
	Edgar and Karp, Peter D and Sander, Chris and Bader, Gary D},
  title = {{The BioPAX community standard for pathway data sharing}},
  journal = {Nat Biotechnol},
  year = {2010},
  volume = {28},
  pages = {935--942},
  number = {9},
  month = sep,
  abstract = {Biological Pathway Exchange (BioPAX) is a standard language to represent
	biological pathways at the molecular and cellular level and to facilitate
	the exchange of pathway data. The rapid growth of the volume of pathway
	data has spurred the development of databases and computational tools
	to aid interpretation; however, use of these data is hampered by
	the current fragmentation of pathway information across many databases
	with incompatible formats. BioPAX, which was created through a community
	process, solves this problem by making pathway data substantially
	easier to collect, index, interpret and share. BioPAX can represent
	metabolic and signaling pathways, molecular and genetic interactions
	and gene regulation networks. Using BioPAX, millions of interactions,
	organized into thousands of pathways, from many organisms are available
	from a growing number of databases. This large amount of pathway
	data in a computable form will support visualization, analysis and
	biological discovery.},
  address = {Computational Biology, Memorial Sloan-Kettering Cancer Center, New
	York, New York, USA.},
  doi = {10.1038/nbt.1666},
  isbn = {1546-1696},
  issn = {1546-1696},
  keywords = {Computational Biology,Computational Biology: methods,Computational
	Biology: standards,Databases as Topic,Information Dissemination,Metabolic
	Networks and Pathways,Programming Languages,Signal Transduction,Software},
  pmid = {20829833},
  publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited.
	All Rights Reserved.},
  shorttitle = {Nat Biotech},
  url = {http://dx.doi.org/10.1038/nbt.1666 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3001121\&tool=pmcentrez\&rendertype=abstract http://view.ncbi.nlm.nih.gov/pubmed/20829833}
}

@ARTICLE{Dogrusoz2009,
  author = {Dogrusoz, Ugur and Cetintas, Ahmet and Demir, Emek and Babur, Ozgun},
  title = {{Algorithms for effective querying of compound graph-based pathway
	databases}},
  journal = {BMC Bioinformatics},
  year = {2009},
  volume = {10},
  pages = {376},
  month = jan,
  abstract = {Graph-based pathway ontologies and databases are widely used to represent
	data about cellular processes. This representation makes it possible
	to programmatically integrate cellular networks and to investigate
	them using the well-understood concepts of graph theory in order
	to predict their structural and dynamic properties. An extension
	of this graph representation, namely hierarchically structured or
	compound graphs, in which a member of a biological network may recursively
	contain a sub-network of a somehow logically similar group of biological
	objects, provides many additional benefits for analysis of biological
	pathways, including reduction of complexity by decomposition into
	distinct components or modules. In this regard, it is essential to
	effectively query such integrated large compound networks to extract
	the sub-networks of interest with the help of efficient algorithms
	and software tools.},
  address = {Computer Engineering Dept, Bilkent University, Center for Bioinformatics,
	Ankara, Turkey. ugur@cs.bilkent.edu.tr},
  doi = {10.1186/1471-2105-10-376},
  isbn = {1471-2105},
  issn = {1471-2105},
  keywords = {Algorithms,Computational Biology,Computational Biology: methods,Computer
	Graphics,Databases,Factual,Protein Interaction Mapping,Signal Transduction,Software},
  language = {en},
  pmid = {19917102},
  publisher = {BioMed Central},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/19917102 /pmc/articles/PMC2784781/?report=abstract}
}

@ARTICLE{Hornbeck2012,
  author = {Hornbeck, Peter V and Kornhauser, Jon M and Tkachev, Sasha and Zhang,
	Bin and Skrzypek, Elzbieta and Murray, Beth and Latham, Vaughan and
	Sullivan, Michael},
  title = {{PhosphoSitePlus: a comprehensive resource for investigating the
	structure and function of experimentally determined post-translational
	modifications in man and mouse.}},
  journal = {Nucleic acids research},
  year = {2012},
  volume = {40},
  pages = {D261--70},
  number = {Database issue},
  month = jan,
  abstract = {PhosphoSitePlus (http://www.phosphosite.org) is an open, comprehensive,
	manually curated and interactive resource for studying experimentally
	observed post-translational modifications, primarily of human and
	mouse proteins. It encompasses 1,30,000 non-redundant modification
	sites, primarily phosphorylation, ubiquitinylation and acetylation.
	The interface is designed for clarity and ease of navigation. From
	the home page, users can launch simple or complex searches and browse
	high-throughput data sets by disease, tissue or cell line. Searches
	can be restricted by specific treatments, protein types, domains,
	cellular components, disease, cell types, cell lines, tissue and
	sequences or motifs. A few clicks of the mouse will take users to
	substrate pages or protein pages with sites, sequences, domain diagrams
	and molecular visualization of side-chains known to be modified;
	to site pages with information about how the modified site relates
	to the functions of specific proteins and cellular processes and
	to curated information pages summarizing the details from one record.
	PyMOL and Chimera scripts that colorize reactive groups on residues
	that are modified can be downloaded. Features designed to facilitate
	proteomic analyses include downloads of modification sites, kinase-substrate
	data sets, sequence logo generators, a Cytoscape plugin and BioPAX
	download to enable pathway visualization of the kinase-substrate
	interactions in PhosphoSitePlus®.},
  doi = {10.1093/nar/gkr1122},
  file = {:home/emek/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hornbeck et al. - 2012 - PhosphoSitePlus a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse.pdf:pdf},
  issn = {1362-4962},
  pmid = {22135298},
  url = {http://nar.oxfordjournals.org/cgi/content/abstract/gkr1122v1}
}

@ARTICLE{Hu2007,
  author = {Hu, Pingzhao and Bader, Gary and Wigle, Dennis A and Emili, Andrew},
  title = {{Computational prediction of cancer-gene function.}},
  journal = {Nature reviews. Cancer},
  year = {2007},
  volume = {7},
  pages = {23--34},
  number = {1},
  month = jan,
  abstract = {Most cancer genes remain functionally uncharacterized in the physiological
	context of disease development. High-throughput molecular profiling
	and interaction studies are increasingly being used to identify clusters
	of functionally linked gene products related to neoplastic cell processes.
	However, in vivo determination of cancer-gene function is laborious
	and inefficient, so accurately predicting cancer-gene function is
	a significant challenge for oncologists and computational biologists
	alike. How can modern computational and statistical methods be used
	to reliably deduce the function(s) of poorly characterized cancer
	genes from the newly available genomic and proteomic datasets? We
	explore plausible solutions to this important challenge.},
  address = {Program in Proteomics and Bioinformatics, Banting and Best Department
	of Medical Research, University of Toronto, Toronto, Ontario, Canada.},
  doi = {10.1038/nrc2036},
  isbn = {1474-175X},
  issn = {1474-175X},
  keywords = {Automated,Biological,Computational Biology,Computational Biology:
	methods,Databases,Gene Expression Profiling,Gene Expression Regulation,Genetic,Genomics,Genomics:
	methods,Humans,Models,Neoplasms,Neoplasms: genetics,Neoplasms: metabolism,Neoplastic,Pattern
	Recognition,Protein,Proteomics,Proteomics: methods},
  pmid = {17167517},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/17167517 http://www.ncbi.nlm.nih.gov/pubmed/17167517}
}

@ARTICLE{VanIersel2012,
  author = {van Iersel, Martijn P and Vill\'{e}ger, Alice C and Czauderna, Tobias
	and Boyd, Sarah E and Bergmann, Frank T and Luna, Augustin and Demir,
	Emek and Sorokin, Anatoly and Dogrusoz, Ugur and Matsuoka, Yukiko
	and Funahashi, Akira and Aladjem, Mirit I and Mi, Huaiyu and Moodie,
	Stuart L and Kitano, Hiroaki and {Le Nov\`{e}re}, Nicolas and Schreiber,
	Falk},
  title = {{Software support for SBGN maps: SBGN-ML and LibSBGN.}},
  journal = {Bioinformatics (Oxford, England)},
  year = {2012},
  pages = {bts270--},
  month = may,
  abstract = {MOTIVATION: LibSBGN is a software library for reading, writing and
	manipulating SBGN (Systems Biology Graphical Notation) maps stored
	using the recently developed SBGN-ML file format. The library (available
	in C++ and Java) makes it easy for developers to add SBGN support
	to their tools, whereas the file format facilitates the exchange
	of maps between compatible software applications. The library also
	supports validation of maps, which simplifies the task of ensuring
	compliance with the detailed SBGN specifications. With this effort
	we hope to increase the adoption of SBGN in bioinformatics tools,
	ultimately enabling more researchers to visualize biological knowledge
	in a precise and unambiguous manner.Availability \& Implementation:
	Milestone 2 was released in December 2011. Source code, example files
	and binaries are freely available under the terms of either the LGPL
	v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net.
	CONTACT: sbgn-libsbgn@lists.sourceforge.net.},
  doi = {10.1093/bioinformatics/bts270},
  file = {:home/emek/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/van Iersel et al. - 2012 - Software support for SBGN maps SBGN-ML and LibSBGN.pdf:pdf},
  issn = {1367-4811},
  pmid = {22581176},
  url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/bts270v1}
}

@ARTICLE{Jones2008,
  author = {Jones, Si\^{a}n and Zhang, Xiaosong and Parsons, D Williams and Lin,
	Jimmy Cheng-Ho and Leary, Rebecca J and Angenendt, Philipp and Mankoo,
	Parminder and Carter, Hannah and Kamiyama, Hirohiko and Jimeno, Antonio
	and Hong, Seung-Mo and Fu, Baojin and Lin, Ming-Tseh and Calhoun,
	Eric S and Kamiyama, Mihoko and Walter, Kimberly and Nikolskaya,
	Tatiana and Nikolsky, Yuri and Hartigan, James and Smith, Douglas
	R and Hidalgo, Manuel and Leach, Steven D and Klein, Alison P and
	Jaffee, Elizabeth M and Goggins, Michael and Maitra, Anirban and
	Iacobuzio-Donahue, Christine and Eshleman, James R and Kern, Scott
	E and Hruban, Ralph H and Karchin, Rachel and Papadopoulos, Nickolas
	and Parmigiani, Giovanni and Vogelstein, Bert and Velculescu, Victor
	E and Kinzler, Kenneth W},
  title = {{Core signaling pathways in human pancreatic cancers revealed by
	global genomic analyses.}},
  journal = {Science (New York, N.Y.)},
  year = {2008},
  volume = {321},
  pages = {1801--6},
  number = {5897},
  month = sep,
  abstract = {There are currently few therapeutic options for patients with pancreatic
	cancer, and new insights into the pathogenesis of this lethal disease
	are urgently needed. Toward this end, we performed a comprehensive
	genetic analysis of 24 pancreatic cancers. We first determined the
	sequences of 23,219 transcripts, representing 20,661 protein-coding
	genes, in these samples. Then, we searched for homozygous deletions
	and amplifications in the tumor DNA by using microarrays containing
	probes for approximately 10(6) single-nucleotide polymorphisms. We
	found that pancreatic cancers contain an average of 63 genetic alterations,
	the majority of which are point mutations. These alterations defined
	a core set of 12 cellular signaling pathways and processes that were
	each genetically altered in 67 to 100\% of the tumors. Analysis of
	these tumors' transcriptomes with next-generation sequencing-by-synthesis
	technologies provided independent evidence for the importance of
	these pathways and processes. Our data indicate that genetically
	altered core pathways and regulatory processes only become evident
	once the coding regions of the genome are analyzed in depth. Dysregulation
	of these core pathways and processes through mutation can explain
	the major features of pancreatic tumorigenesis.},
  address = {Sol Goldman Pancreatic Cancer Research Center, Ludwig Center and
	Howard Hughes Medical Institute at the Johns Hopkins Kimmel Cancer
	Center, Baltimore, MD 21231, USA.},
  doi = {10.1126/science.1164368},
  isbn = {1095-9203},
  issn = {1095-9203},
  keywords = {Adenocarcinoma,Adenocarcinoma: etiology,Adenocarcinoma: genetics,Adenocarcinoma:
	metabolism,Algorithms,Carcinoma,Computational Biology,Gene Amplification,Gene
	Expression Profiling,Genome,Human,Humans,Missense,Models,Molecular,Mutation,Oligonucleotide
	Array Sequence Analysis,Pancreatic Ductal,Pancreatic Ductal: etiology,Pancreatic
	Ductal: genetics,Pancreatic Ductal: metabolism,Pancreatic Neoplasms,Pancreatic
	Neoplasms: etiology,Pancreatic Neoplasms: genetics,Pancreatic Neoplasms:
	metabolism,Point Mutation,Polymorphism,Sequence Deletion,Signal Transduction,Signal
	Transduction: genetics,Single Nucleotide},
  pmid = {18772397},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/18772397 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2848990\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Kelder2010,
  author = {Kelder, Thomas and Conklin, Bruce R and Evelo, Chris T and Pico,
	Alexander R},
  title = {{Finding the right questions: exploratory pathway analysis to enhance
	biological discovery in large datasets.}},
  journal = {PLoS biology},
  year = {2010},
  volume = {8},
  pages = {5},
  number = {8},
  month = jan,
  abstract = {This Essay discusses the role of pathways for exploratory data analysis
	in present-day biology.},
  doi = {10.1371/journal.pbio.1000472},
  editor = {Eisen, Jonathan A.},
  issn = {1545-7885},
  keywords = {Computational Biology,Computational Biology: methods,Computer Graphics,Database
	Management Systems,Databases, Factual,Databases, Genetic,Humans,Models,
	Biological,Software,Software Design,User-Computer Interface},
  pmid = {20824171},
  publisher = {Public Library of Science},
  url = {http://dx.plos.org/10.1371/journal.pbio.1000472}
}

@ARTICLE{Kirouac2012,
  author = {Kirouac, Daniel C and Saez-Rodriguez, Julio and Swantek, Jennifer
	and Burke, John M and Lauffenburger, Douglas A and Sorger, Peter
	K},
  title = {{Creating and analyzing pathway and protein interaction compendia
	for modelling signal transduction networks.}},
  journal = {BMC systems biology},
  year = {2012},
  volume = {6},
  pages = {29},
  number = {1},
  month = may,
  abstract = {ABSTRACT: BACKGROUND: Understanding the information-processing capabilities
	of signal transduction networks, how those networks are disrupted
	in disease, and rationally designing therapies to manipulate diseased
	states require systematic and accurate reconstruction of network
	topology. Data on networks central to human physiology, such as the
	inflammatory signalling networks analyzed here, are found in a multiplicity
	of on-line resources of pathway and interactome databases (Cancer
	CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID),
	PANTHER, Reactome, I2D, and STRING). We sought to determine whether
	these databases contain overlapping information and whether they
	can be used to construct high reliability prior knowledge networks
	for subsequent modeling of experimental data. RESULTS: We have assembled
	an ensemble network from multiple on-line sources representing a
	significant portion of all machine-readable and reconcilable human
	knowledge on proteins and protein interactions involved in inflammation.
	This ensemble network has many features expected of complex signalling
	networks assembled from high-throughput data: a power law distribution
	of both node degree and edge annotations, and topological features
	of a "bow tie" architecture in which diverse pathways converge on
	a highly conserved set of enzymatic cascades focused around PI3K/AKT,
	MAPK/ERK, JAK/STAT, NFkappaB, and apoptotic signaling. Individual
	pathways exhibit "fuzzy" modularity that is statistically significant
	but still involving a majority of "cross-talk" interactions. However,
	we find that the most widely used pathway databases are highly inconsistent
	with respect to the actual constituents and interactions in this
	network. Using a set of growth factor signalling networks as examples,
	we find a multiplicity of network topologies in which receptors couple
	to downstream components through myriad alternate paths. Many of
	these paths are inconsistent with well-established mechanistic features
	of signalling networks, such as a requirement for a transmembrane
	receptor in sensing extracellular ligands. CONCLUSIONS: Wide inconsistencies
	among interaction databases, pathway annotations, and the numbers
	and identities of nodes associated with a given pathway pose a major
	challenge for deriving causal and mechanistic insight from network
	graphs. We speculate that these inconsistencies are at least partially
	attributable to cell, and context-specificity of cellular signal
	transduction, which is largely unaccounted for in available databases,
	but the absence of standardized vocabularies is an additional confounding
	factor. As a result of discrepant annotations, it is very difficult
	to identify biologically meaningful pathways from interactome networks
	a priori. However, by incorporating prior knowledge, it is possible
	to successively build out network complexity with high confidence
	from a simple linear signal transduction scaffold. Such reduced complexity
	networks appear suitable for use in mechanistic models while being
	richer and better justified than the simple linear pathways usually
	depicted in diagrams of signal transduction.},
  doi = {10.1186/1752-0509-6-29},
  file = {:home/emek/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kirouac et al. - 2012 - Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks.pdf:pdf},
  issn = {1752-0509},
  pmid = {22548703},
  url = {http://www.biomedcentral.com/1752-0509/6/29}
}

@ARTICLE{Li2010,
  author = {Li, Chen and Donizelli, Marco and Rodriguez, Nicolas and Dharuri,
	Harish and Endler, Lukas and Chelliah, Vijayalakshmi and Li, Lu and
	He, Enuo and Henry, Arnaud and Stefan, Melanie I and Snoep, Jacky
	L and Hucka, Michael and {Le Nov\`{e}re}, Nicolas and Laibe, Camille},
  title = {{BioModels Database: An enhanced, curated and annotated resource
	for published quantitative kinetic models.}},
  journal = {BMC systems biology},
  year = {2010},
  volume = {4},
  pages = {92},
  number = {1},
  month = jan,
  abstract = {Quantitative models of biochemical and cellular systems are used to
	answer a variety of questions in the biological sciences. The number
	of published quantitative models is growing steadily thanks to increasing
	interest in the use of models as well as the development of improved
	software systems and the availability of better, cheaper computer
	hardware. To maximise the benefits of this growing body of models,
	the field needs centralised model repositories that will encourage,
	facilitate and promote model dissemination and reuse. Ideally, the
	models stored in these repositories should be extensively tested
	and encoded in community-supported and standardised formats. In addition,
	the models and their components should be cross-referenced with other
	resources in order to allow their unambiguous identification.},
  doi = {10.1186/1752-0509-4-92},
  issn = {1752-0509},
  keywords = {Biochemical Processes,Biochemical Processes: physiology,Databases,
	Factual,Internet,Kinetics,Models, Biological,Systems Biology,Systems
	Biology: methods},
  pmid = {20587024},
  url = {http://www.biomedcentral.com/1752-0509/4/92}
}

@ARTICLE{Maayan2009,
  author = {Ma'ayan, Avi},
  title = {{Insights into the organization of biochemical regulatory networks
	using graph theory analyses.}},
  journal = {The Journal of biological chemistry},
  year = {2009},
  volume = {284},
  pages = {5451--5},
  number = {9},
  month = feb,
  abstract = {Graph theory has been a valuable mathematical modeling tool to gain
	insights into the topological organization of biochemical networks.
	There are two types of insights that may be obtained by graph theory
	analyses. The first provides an overview of the global organization
	of biochemical networks; the second uses prior knowledge to place
	results from multivariate experiments, such as microarray data sets,
	in the context of known pathways and networks to infer regulation.
	Using graph analyses, biochemical networks are found to be scale-free
	and small-world, indicating that these networks contain hubs, which
	are proteins that interact with many other molecules. These hubs
	may interact with many different types of proteins at the same time
	and location or at different times and locations, resulting in diverse
	biological responses. Groups of components in networks are organized
	in recurring patterns termed network motifs such as feedback and
	feed-forward loops. Graph analysis revealed that negative feedback
	loops are less common and are present mostly in proximity to the
	membrane, whereas positive feedback loops are highly nested in an
	architecture that promotes dynamical stability. Cell signaling networks
	have multiple pathways from some input receptors and few from others.
	Such topology is reminiscent of a classification system. Signaling
	networks display a bow-tie structure indicative of funneling information
	from extracellular signals and then dispatching information from
	a few specific central intracellular signaling nexuses. These insights
	show that graph theory is a valuable tool for gaining an understanding
	of global regulatory features of biochemical networks.},
  doi = {10.1074/jbc.R800056200},
  issn = {0021-9258},
  keywords = {Animals,Computer Graphics,Gene Regulatory Networks,Humans,Mathematics,Metabolic
	Networks and Pathways,Models, Biological,Signal Transduction},
  pmid = {18940806},
  url = {http://www.jbc.org/cgi/content/abstract/284/9/5451}
}

@ARTICLE{Mi2010,
  author = {Mi, Huaiyu and Dong, Qing and Muruganujan, Anushya and Gaudet, Pascale
	and Lewis, Suzanna and Thomas, Paul D},
  title = {{PANTHER version 7: improved phylogenetic trees, orthologs and collaboration
	with the Gene Ontology Consortium.}},
  journal = {Nucleic acids research},
  year = {2010},
  volume = {38},
  pages = {D204--10},
  number = {Database issue},
  month = jan,
  abstract = {Protein Analysis THrough Evolutionary Relationships (PANTHER) is a
	comprehensive software system for inferring the functions of genes
	based on their evolutionary relationships. Phylogenetic trees of
	gene families form the basis for PANTHER and these trees are annotated
	with ontology terms describing the evolution of gene function from
	ancestral to modern day genes. One of the main applications of PANTHER
	is in accurate prediction of the functions of uncharacterized genes,
	based on their evolutionary relationships to genes with functions
	known from experiment. The PANTHER website, freely available at http://www.pantherdb.org,
	also includes software tools for analyzing genomic data relative
	to known and inferred gene functions. Since 2007, there have been
	several new developments to PANTHER: (i) improved phylogenetic trees,
	explicitly representing speciation and gene duplication events, (ii)
	identification of gene orthologs, including least diverged orthologs
	(best one-to-one pairs), (iii) coverage of more genomes (48 genomes,
	up to 87\% of genes in each genome; see http://www.pantherdb.org/panther/summaryStats.jsp),
	(iv) improved support for alternative database identifiers for genes,
	proteins and microarray probes and (v) adoption of the SBGN standard
	for display of biological pathways. In addition, PANTHER trees are
	being annotated with gene function as part of the Gene Ontology Reference
	Genome project, resulting in an increasing number of curated functional
	annotations.},
  address = {Evolutionary Systems Biology Group, SRI International, Lawrence Berkeley
	National Laboratory, USA.},
  doi = {10.1093/nar/gkp1019},
  isbn = {1362-4962},
  issn = {1362-4962},
  keywords = {Algorithms,Amino Acid,Animals,Computational Biology,Computational
	Biology: methods,Computational Biology: trends,Databases,Evolution,Genetic,Humans,Information
	Storage and Retrieval,Information Storage and Retrieval: methods,Internet,Molecular,Nucleic
	Acid,Phylogeny,Protein,Protein Structure,Sequence Analysis,Sequence
	Homology,Software,Tertiary},
  pmid = {20015972},
  url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2808919\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{mi2011,
  author = {Mi, Huaiyu and Muruganujan, Anushya and Demir, Emek and Matsuoka,
	Yukiko and Funahashi, Akira and Kitano, Hiroaki and Thomas, Paul
	D},
  title = {{BioPAX support in CellDesigner.}},
  journal = {Bioinformatics (Oxford, England)},
  year = {2011},
  volume = {27},
  pages = {3437--8},
  number = {24},
  month = dec,
  abstract = {MOTIVATION: BioPAX is a standard language for representing and exchanging
	models of biological processes at the molecular and cellular levels.
	It is widely used by different pathway databases and genomics data
	analysis software. Currently, the primary source of BioPAX data is
	direct exports from the curated pathway databases. It is still uncommon
	for wet-lab biologists to share and exchange pathway knowledge using
	BioPAX. Instead, pathways are usually represented as informal diagrams
	in the literature. In order to encourage formal representation of
	pathways, we describe a software package that allows users to create
	pathway diagrams using CellDesigner, a user-friendly graphical pathway-editing
	tool and save the pathway data in BioPAX Level 3 format. AVAILABILITY:
	The plug-in is freely available and can be downloaded at ftp://ftp.pantherdb.org/CellDesigner/plugins/BioPAX/
	CONTACT: huaiyumi@usc.edu SUPPLEMENTARY INFORMATION: Supplementary
	data are available at Bioinformatics online.},
  doi = {10.1093/bioinformatics/btr586},
  issn = {1367-4811},
  keywords = {Animals,Biological,Computational Biology,Databases,Factual,Humans,Metabolic
	Networks and Pathways,Models,Signal Transduction,Software},
  pmid = {22021903},
  url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btr586v1 http://bioinformatics.oxfordjournals.org/cgi/content/abstract/27/24/3437 http://www.ncbi.nlm.nih.gov/pubmed/22021903}
}

@ARTICLE{Schaefer2009,
  author = {Schaefer, Carl F and Anthony, Kira and Krupa, Shiva and Buchoff,
	Jeffrey and Day, Matthew and Hannay, Timo and Buetow, Kenneth H},
  title = {{PID: the Pathway Interaction Database.}},
  journal = {Nucleic acids research},
  year = {2009},
  volume = {37},
  pages = {D674--9},
  number = {Database issue},
  month = jan,
  abstract = {The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is
	a freely available collection of curated and peer-reviewed pathways
	composed of human molecular signaling and regulatory events and key
	cellular processes. Created in a collaboration between the US National
	Cancer Institute and Nature Publishing Group, the database serves
	as a research tool for the cancer research community and others interested
	in cellular pathways, such as neuroscientists, developmental biologists
	and immunologists. PID offers a range of search features to facilitate
	pathway exploration. Users can browse the predefined set of pathways
	or create interaction network maps centered on a single molecule
	or cellular process of interest. In addition, the batch query tool
	allows users to upload long list(s) of molecules, such as those derived
	from microarray experiments, and either overlay these molecules onto
	predefined pathways or visualize the complete molecular connectivity
	map. Users can also download molecule lists, citation lists and complete
	database content in extensible markup language (XML) and Biological
	Pathways Exchange (BioPAX) Level 2 format. The database is updated
	with new pathway content every month and supplemented by specially
	commissioned articles on the practical uses of other relevant online
	tools.},
  address = {National Cancer Institute, Center for Biomedical Informatics and
	Information Technology, Rockville MD, USA. schaefec@mail.nih.gov},
  doi = {10.1093/nar/gkn653},
  isbn = {1362-4962},
  issn = {1362-4962},
  keywords = {Biological Transport,Cell Physiological Processes,Databases,Factual,Gene
	Expression Regulation,Humans,Internet,Protein Interaction Mapping,Proteins,Proteins:
	chemistry,Proteins: metabolism,RNA,RNA: chemistry,RNA: metabolism,Signal
	Transduction,User-Computer Interface},
  pmid = {18832364},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/18832364 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2686461\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Vaske2010,
  author = {Vaske, Charles J and Benz, Stephen C and Sanborn, J Zachary and Earl,
	Dent and Szeto, Christopher and Zhu, Jingchun and Haussler, David
	and Stuart, Joshua M},
  title = {{Inference of patient-specific pathway activities from multi-dimensional
	cancer genomics data using PARADIGM.}},
  journal = {Bioinformatics (Oxford, England)},
  year = {2010},
  volume = {26},
  pages = {i237--45},
  number = {12},
  month = jun,
  abstract = {High-throughput data is providing a comprehensive view of the molecular
	changes in cancer tissues. New technologies allow for the simultaneous
	genome-wide assay of the state of genome copy number variation, gene
	expression, DNA methylation and epigenetics of tumor samples and
	cancer cell lines. Analyses of current data sets find that genetic
	alterations between patients can differ but often involve common
	pathways. It is therefore critical to identify relevant pathways
	involved in cancer progression and detect how they are altered in
	different patients.},
  address = {Howard Hughes Medical Institute, UC Santa Cruz, CA, USA.},
  doi = {10.1093/bioinformatics/btq182},
  isbn = {1367-4811},
  issn = {1367-4811},
  keywords = {Breast Neoplasms,Breast Neoplasms: genetics,DNA Copy Number Variations,Female,Gene
	Expression Profiling,Gene Expression Profiling: methods,Genomics,Genomics:
	methods,Glioblastoma,Glioblastoma: genetics,Humans,Neoplasms,Neoplasms:
	genetics,Software},
  pmid = {20529912},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/20529912 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2881367\&tool=pmcentrez\&rendertype=abstract}
}

@ARTICLE{Yu2012,
  author = {Yu, Namhee and Seo, Jihae and Rho, Kyoohyoung and Jang, Yeongjun
	and Park, Jinah and Kim, Wan Kyu and Lee, Sanghyuk},
  title = {{hiPathDB: a human-integrated pathway database with facile visualization.}},
  journal = {Nucleic acids research},
  year = {2012},
  volume = {40},
  pages = {D797--802},
  number = {Database issue},
  month = jan,
  abstract = {One of the biggest challenges in the study of biological regulatory
	networks is the systematic organization and integration of complex
	interactions taking place within various biological pathways. Currently,
	the information of the biological pathways is dispersed in multiple
	databases in various formats. hiPathDB is an integrated pathway database
	that combines the curated human pathway data of NCI-Nature PID, Reactome,
	BioCarta and KEGG. In total, it includes 1661 pathways consisting
	of 8976 distinct physical entities. hiPathDB provides two different
	types of integration. The pathway-level integration, conceptually
	a simple collection of individual pathways, was achieved by devising
	an elaborate model that takes distinct features of four databases
	into account and subsequently reformatting all pathways in accordance
	with our model. The entity-level integration creates a single unified
	pathway that encompasses all pathways by merging common components.
	Even though the detailed molecular-level information such as complex
	formation or post-translational modifications tends to be lost, such
	integration makes it possible to investigate signaling network over
	the entire pathways and allows identification of pathway cross-talks.
	Another strong merit of hiPathDB is the built-in pathway visualization
	module that supports explorative studies of complex networks in an
	interactive fashion. The layout algorithm is optimized for virtually
	automatic visualization of the pathways. hiPathDB is available at
	http://hiPathDB.kobic.re.kr.},
  doi = {10.1093/nar/gkr1127},
  issn = {1362-4962},
  keywords = {Biological,Computer Graphics,Databases,Factual,Humans,Internet,Models,Signal
	Transduction,Systems Integration,User-Computer Interface},
  pmid = {22123737},
  url = {http://nar.oxfordjournals.org/cgi/content/abstract/40/D1/D797 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3245021\&tool=pmcentrez\&rendertype=abstract}
}

@article{Searls2005,
abstract = {The effective integration of data and knowledge from many disparate sources will be crucial to future drug discovery. Data integration is a key element of conducting scientific investigations with modern platform technologies, managing increasingly complex discovery portfolios and processes, and fully realizing economies of scale in large enterprises. However, viewing data integration as simply an 'IT problem' underestimates the novel and serious scientific and management challenges it embodies - challenges that could require significant methodological and even cultural changes in our approach to data.},
author = {Searls, David B},
doi = {10.1038/nrd1608},
issn = {1474-1776},
journal = {Nature reviews. Drug discovery},
keywords = {Data Collection,Data Collection: methods,Database Management Systems,Database Management Systems: trends,Drug Design,Drug Industry,Drug Industry: methods,Models,Systems Integration,Terminology as Topic,Theoretical},
month = jan,
number = {1},
pages = {45--58},
pmid = {15688072},
title = {{Data integration: challenges for drug discovery.}},
url = {http://dx.doi.org/10.1038/nrd1608 http://www.ncbi.nlm.nih.gov/pubmed/15688072},
volume = {4},
year = {2005}
}
@article{Sorger2005,
abstract = {To tackle the complexity inherent in understanding large networks of interacting biomolecules, systems biology emphasizes cybernetic and systems theoretical approaches. The resulting focus on organization independent of physical manifestation threatens to throw away all that has been learned from molecular studies and ignores the reality that biologists are drawn together more by a shared interest in mechanism and structure than anything else. The field of reaction engineering suggests a reductionist approach to systems biology that fits easily within existing molecular paradigms but that can nonetheless be integrated into expansive physiological perspectives through the use of multi-scale modeling.},
author = {Sorger, Peter K},
doi = {10.1016/j.ceb.2004.12.012},
issn = {0955-0674},
journal = {Current opinion in cell biology},
keywords = {Animals,Biological,Computational Biology,Computational Biology: methods,Computer Simulation,Models,Molecular Biology,Molecular Biology: methods,Software,Statistics as Topic,Systems Biology,Systems Theory,Theoretical},
month = feb,
number = {1},
pages = {9--11},
pmid = {15661513},
title = {{A reductionist's systems biology: opinion.}},
url = {http://dx.doi.org/10.1016/j.ceb.2004.12.012 http://www.ncbi.nlm.nih.gov/pubmed/15661513},
volume = {17},
year = {2005}
}

@article{Babur2010,
abstract = {Proteins that modulate the activity of transcription factors, often called modulators, play a critical role in creating tissue- and context-specific gene expression responses to the signals cells receive. GEM (Gene Expression Modulation) is a probabilistic framework that predicts modulators, their affected targets and mode of action by combining gene expression profiles, protein-protein interactions and transcription factor-target relationships. Using GEM, we correctly predicted a significant number of androgen receptor modulators and observed that most modulators can both act as co-activators and co-repressors for different target genes.},
address = {Center for Bioinformatics and Computer Engineering Department, Bilkent University, Ankara 06800, Turkey.},
author = {Babur, Ozg\"{u}n and Demir, Emek and G\"{o}nen, Mithat and Sander, Chris and Dogrusoz, Ugur},
doi = {10.1093/nar/gkq287},
isbn = {1362-4962},
issn = {1362-4962},
journal = {Nucleic Acids Res},
keywords = {Algorithms,Androgen,Androgen: metabolism,Gene Expression Profiling,Gene Expression Regulation,Genetic,Models,Probability,Protein Interaction Mapping,Receptors,Transcription,Transcription Factors,Transcription Factors: metabolism},
month = sep,
number = {17},
pages = {5648--5656},
pmid = {20466809},
title = {{Discovering modulators of gene expression}},
url = {http://nar.oxfordjournals.org/cgi/content/abstract/38/17/5648 http://view.ncbi.nlm.nih.gov/pubmed/20466809},
volume = {38},
year = {2010}
}

@article{Sreenivasaiah2012,
abstract = {Integrated Pathway Resources, Analysis and Visualization System (iPAVS) is an integrated biological pathway database designed to support pathway discovery in the fields of proteomics, transcriptomics, metabolomics and systems biology. The key goal of IPAVS is to provide biologists access to expert-curated pathways from experimental data belonging to specific biological contexts related to cell types, tissues, organs and diseases. IPAVS currently integrates over 500 human pathways (consisting of 24, 574 interactions) that include metabolic-, signaling- and disease-related pathways, drug-action pathways and several large process maps collated from other pathway resources. IPAVS web interface allows biologists to browse and search pathway resources and provides tools for data import, management, visualization and analysis to support the interpretation of biological data in light of cellular processes. Systems Biology Graphical Notations (SBGN) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway notations are used for the visual display of pathway information. The integrated datasets in IPAVS are made available in several standard data formats that can be downloaded. IPAVS is available at: http://ipavs.cidms.org.},
author = {Sreenivasaiah, Pradeep Kumar and Rani, Shilpa and Cayetano, Joseph and Arul, Novino and Kim, Do Han},
doi = {10.1093/nar/gkr1208},
issn = {1362-4962},
journal = {Nucleic acids research},
keywords = {Computer Graphics,Databases,Disease,Factual,Humans,Internet,Metabolic Networks and Pathways,Pharmacological Phenomena,Signal Transduction,Systems Biology,Systems Integration},
month = jan,
number = {Database issue},
pages = {D803--8},
pmid = {22140115},
title = {{IPAVS: Integrated Pathway Resources, Analysis and Visualization System.}},
url = {http://nar.oxfordjournals.org/cgi/content/abstract/40/D1/D803 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3245119\&tool=pmcentrez\&rendertype=abstract},
volume = {40},
year = {2012}
}

@article{TCGA2008,
abstract = {Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas (TCGA) pilot project aims to assess the value of large-scale multi-dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas--the most common type of adult brain cancer--and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol-3-OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.},
author = {The Cancer Genome Atlas Network},
doi = {10.1038/nature07385},
isbn = {1476-4687},
issn = {1476-4687},
journal = {Nature},
keywords = {80 and over,Adolescent,Adult,Aged,Brain Neoplasms,Brain Neoplasms: genetics,DNA Methylation,DNA Modification Methylases,DNA Modification Methylases: genetics,DNA Repair,DNA Repair Enzymes,DNA Repair Enzymes: genetics,DNA Repair: genetics,Female,Gene Dosage,Gene Expression Regulation,Genes,Genome,Genomics,Glioblastoma,Glioblastoma: genetics,Human,Human: genetics,Humans,Male,Middle Aged,Models,Molecular,Mutation,Mutation: genetics,Neoplastic,Neurofibromin 1,Neurofibromin 1: genetics,Phosphatidylinositol 3-Kinases,Phosphatidylinositol 3-Kinases: genetics,Protein Structure,Retrospective Studies,Signal Transduction,Signal Transduction: genetics,Tertiary,Tumor Suppressor,Tumor Suppressor Proteins,Tumor Suppressor Proteins: genetics,erbB-1,erbB-1: genetics},
month = oct,
number = {7216},
pages = {1061--8},
pmid = {18772890},
title = {{Comprehensive genomic characterization defines human glioblastoma genes and core pathways.}},
url = {http://view.ncbi.nlm.nih.gov/pubmed/18772890 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2671642\&tool=pmcentrez\&rendertype=abstract},
volume = {455},
year = {2008}
}
@article{Le_Novère2009,
abstract = {Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.},
address = {EMBL European Bioinformatics Institute, Hinxton, UK. lenov@ebi.ac.uk},
author = {{Le Nov\`{e}re}, Nicolas and Hucka, Michael and Mi, Huaiyu and Moodie, Stuart and Schreiber, Falk and Sorokin, Anatoly and Demir, Emek and Wegner, Katja and Aladjem, Mirit I and Wimalaratne, Sarala M and Bergman, Frank T and Gauges, Ralph and Ghazal, Peter and Kawaji, Hideya and Li, Lu and Matsuoka, Yukiko and Vill\'{e}ger, Alice and Boyd, Sarah E and Calzone, Laurence and Courtot, Melanie and Dogrusoz, Ugur and Freeman, Tom C and Funahashi, Akira and Ghosh, Samik and Jouraku, Akiya and Kim, Sohyoung and Kolpakov, Fedor and Luna, Augustin and Sahle, Sven and Schmidt, Esther and Watterson, Steven and Wu, Guanming and Goryanin, Igor and Kell, Douglas B and Sander, Chris and Sauro, Herbert and Snoep, Jacky L and Kohn, Kurt and Kitano, Hiroaki},
doi = {10.1038/nbt.1558},
isbn = {1546-1696},
issn = {1546-1696},
journal = {Nat Biotechnol},
keywords = {20th Century,Computer Graphics,Computer Graphics: history,History,Internet,Software,Systems Biology,Systems Biology: history},
month = aug,
number = {8},
pages = {735--741},
pmid = {19668183},
publisher = {Nature Publishing Group},
shorttitle = {Nat Biotech},
title = {{The Systems Biology Graphical Notation}},
url = {http://view.ncbi.nlm.nih.gov/pubmed/19668183 http://dx.doi.org/10.1038/nbt.1558},
volume = {27},
year = {2009}
}

@article{Subramanian2005,
abstract = {Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.},
address = {Broad Institute of Massachusetts Institute of Technology and Harvard, 320 Charles Street, Cambridge, MA 02141, USA.},
author = {Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K and Mukherjee, Sayan and Ebert, Benjamin L and Gillette, Michael A and Paulovich, Amanda and Pomeroy, Scott L and Golub, Todd R and Lander, Eric S and Mesirov, Jill P},
doi = {10.1073/pnas.0506580102},
isbn = {0027-8424},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Acute,Acute: genetics,Cell Line,Female,Gene Expression Profiling,Gene Expression Profiling: methods,Genes,Genome,Humans,Leukemia,Lung Neoplasms,Lung Neoplasms: genetics,Lung Neoplasms: mortality,Male,Myeloid,Oligonucleotide Array Sequence Analysis,Precursor Cell Lymphoblastic Leukemia-Lymphoma,Precursor Cell Lymphoblastic Leukemia-Lymphoma: ge,Tumor,p53,p53: physiology},
month = oct,
number = {43},
pages = {15545--50},
pmid = {16199517},
title = {{Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.}},
url = {http://view.ncbi.nlm.nih.gov/pubmed/16199517 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1239896\&tool=pmcentrez\&rendertype=abstract},
volume = {102},
year = {2005}
}

@article{Kerrien2007,
abstract = {Molecular interaction Information is a key resource in modern biomedical research. Publicly available data have previously been provided in a broad array of diverse formats, making access to this very difficult. The publication and wide implementation of the Human Proteome Organisation Proteomics Standards Initiative Molecular Interactions (HUPO PSI-MI) format in 2004 was a major step towards the establishment of a single, unified format by which molecular interactions should be presented, but focused purely on protein-protein interactions.},
address = {European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK. skerrien@ebi.ac.uk},
author = {Kerrien, Samuel and Orchard, Sandra and Montecchi-Palazzi, Luisa and Aranda, Bruno and Quinn, Antony F and Vinod, Nisha and Bader, Gary D and Xenarios, Ioannis and Wojcik, J\'{e}r\^{o}me and Sherman, David and Tyers, Mike and Salama, John J and Moore, Susan and Ceol, Arnaud and Chatr-Aryamontri, Andrew and Oesterheld, Matthias and St\"{u}mpflen, Volker and Salwinski, Lukasz and Nerothin, Jason and Cerami, Ethan and Cusick, Michael E and Vidal, Marc and Gilson, Michael and Armstrong, John and Woollard, Peter and Hogue, Christopher and Eisenberg, David and Cesareni, Gianni and Apweiler, Rolf and Hermjakob, Henning},
isbn = {1741-7007},
journal = {BMC Biol},
pages = {44},
title = {{Broadening the horizon--level 2.5 of the HUPO-PSI format for molecular interactions}},
url = {http://view.ncbi.nlm.nih.gov/pubmed/17925023},
volume = {5},
year = {2007}
}