Finding function: evaluation methods for functional genomic data

[1]  Robert E. Schapire,et al.  Hierarchical multi-label prediction of gene function , 2006, Bioinform..

[2]  William Stafford Noble,et al.  Choosing negative examples for the prediction of protein-protein interactions , 2006, BMC Bioinformatics.

[3]  Frederick P Roth,et al.  Discovering functional relationships: biochemistry versus genetics. , 2005, Trends in genetics : TIG.

[4]  Ian M. Donaldson,et al.  The Biomolecular Interaction Network Database and related tools 2005 update , 2004, Nucleic Acids Res..

[5]  Yanjun Qi,et al.  Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources , 2004, Pacific Symposium on Biocomputing.

[6]  William Stafford Noble,et al.  Kernel methods for predicting protein-protein interactions , 2005, ISMB.

[7]  Haruki Nakamura,et al.  Filtering high-throughput protein-protein interaction data using a combination of genomic features , 2005, BMC Bioinformatics.

[8]  Mark Gerstein,et al.  Information assessment on predicting protein-protein interactions , 2004, BMC Bioinformatics.

[9]  M. Gerstein,et al.  Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. , 2004, Current opinion in microbiology.

[10]  Arun K. Ramani,et al.  Protein interaction networks from yeast to human. , 2004, Current opinion in structural biology.

[11]  Junguk Hur,et al.  A graph-theoretic modeling on GO space for biological interpretation of gene clusters , 2004, Bioinform..

[12]  Nello Cristianini,et al.  Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast , 2003, Pacific Symposium on Biocomputing.

[13]  G. Sumara,et al.  A Probabilistic Functional Network of Yeast Genes , 2004 .

[14]  Yoshihiro Yamanishi,et al.  Protein network inference from multiple genomic data: a supervised approach , 2004, ISMB/ECCB.

[15]  M. Gerstein,et al.  A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data , 2003, Science.

[16]  E. O’Shea,et al.  Global analysis of protein localization in budding yeast , 2003, Nature.

[17]  Amanda Clare,et al.  Predicting gene function in Saccharomyces cerevisiae , 2003, ECCB.

[18]  Carole A. Goble,et al.  Investigating Semantic Similarity Measures Across the Gene Ontology: The Relationship Between Sequence and Annotation , 2003, Bioinform..

[19]  A. Owen,et al.  A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae) , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Shmuel Sattath,et al.  How reliable are experimental protein-protein interaction data? , 2003, Journal of molecular biology.

[21]  M. Tyers,et al.  The GRID: The General Repository for Interaction Datasets , 2003, Genome Biology.

[22]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[23]  Gary D Bader,et al.  Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry , 2002, Nature.

[24]  P. Bork,et al.  Functional organization of the yeast proteome by systematic analysis of protein complexes , 2002, Nature.

[25]  Jason Weston,et al.  Learning Gene Functional Classifications from Multiple Data Types , 2002, J. Comput. Biol..

[26]  Gary D Bader,et al.  A Combined Experimental and Computational Strategy to Define Protein Interaction Networks for Peptide Recognition Modules , 2001, Science.

[27]  D. Botstein,et al.  Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p. , 2001, Molecular biology of the cell.

[28]  R. Ozawa,et al.  A comprehensive two-hybrid analysis to explore the yeast protein interactome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[29]  J. E. Kranz,et al.  YPD, PombePD and WormPD: model organism volumes of the BioKnowledge library, an integrated resource for protein information. , 2001, Nucleic acids research.

[30]  Marek S. Skrzypek,et al.  YPDTM, PombePDTM and WormPDTM: model organism volumes of the BioKnowledgeTM Library, an integrated resource for protein information , 2001, Nucleic Acids Res..

[31]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[32]  James R. Knight,et al.  A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.

[33]  Ioannis Xenarios,et al.  DIP: the Database of Interacting Proteins , 2000, Nucleic Acids Res..

[34]  Kara Dolinski,et al.  Integrating functional genomic information into the Saccharomyces Genome Database , 2000, Nucleic Acids Res..

[35]  Dmitrij Frishman,et al.  MIPS: a database for genomes and protein sequences , 2000, Nucleic Acids Res..

[36]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[37]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.