Predicting Co-Complexed Protein Pairs from Heterogeneous Data
暂无分享,去创建一个
[1] Sean R. Collins,et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae , 2006, Nature.
[2] D. Goldberg,et al. Assessing experimentally derived interactions in a small world , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[3] James R. Knight,et al. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.
[4] E. O’Shea,et al. Global analysis of protein localization in budding yeast , 2003, Nature.
[5] P. Bork,et al. Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.
[6] S. Stevens,et al. Purification of the yeast U4/U6.U5 small nuclear ribonucleoprotein particle and identification of its proteins. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[7] Arun K. Ramani,et al. Exploiting the co-evolution of interacting proteins to discover interaction specificity. , 2003, Journal of molecular biology.
[8] 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.
[9] M. Gerstein,et al. Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. , 2004, Genome research.
[10] A. Valencia,et al. In silico two‐hybrid system for the selection of physically interacting protein pairs , 2002, Proteins.
[11] Nicola J. Rinaldi,et al. Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.
[12] Gunnar Rätsch,et al. Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..
[13] D. Botstein,et al. The transcriptional program of sporulation in budding yeast. , 1998, Science.
[14] Ian M. Donaldson,et al. BIND: THE BIOMOLECULAR INTERACTION DATABASE , 2001 .
[15] Ziv Bar-Joseph,et al. Evaluation of different biological data and computational classification methods for use in protein interaction prediction , 2006, Proteins.
[16] William Stafiord Noble,et al. Support vector machine applications in computational biology , 2004 .
[17] Mike Tyers,et al. BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..
[18] David Botstein,et al. GO: : TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes , 2004, Bioinform..
[19] Mark Gerstein,et al. Information assessment on predicting protein-protein interactions , 2004, BMC Bioinformatics.
[20] J D Beggs,et al. Functional analyses of interacting factors involved in both pre-mRNA splicing and cell cycle progression in Saccharomyces cerevisiae. , 2000, RNA.
[21] Lars Malmström,et al. The Yeast Resource Center Public Data Repository , 2004, Nucleic Acids Res..
[22] Douglas L. Brutlag,et al. Remote homology detection: a motif based approach , 2003, ISMB.
[23] D. Eisenberg,et al. Detecting protein function and protein-protein interactions from genome sequences. , 1999, Science.
[24] Haidong Wang,et al. Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale , 2004, NIPS.
[25] P. Bork,et al. Functional organization of the yeast proteome by systematic analysis of protein complexes , 2002, Nature.
[26] T. Ito,et al. Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[27] Michael I. Jordan,et al. Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.
[28] M. Gerstein,et al. A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data , 2003, Science.
[29] William Stafford Noble,et al. Support vector machine , 2013 .
[30] Yudong D. He,et al. Functional Discovery via a Compendium of Expression Profiles , 2000, Cell.
[31] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[32] Katsumi Isono,et al. Identification and comparative analysis of the large subunit mitochondrial ribosomal proteins of Neurospora crassa. , 2006, FEMS microbiology letters.
[33] Frederick P. Roth,et al. Predicting co-complexed protein pairs using genomic and proteomic data integration , 2004, BMC Bioinformatics.
[34] Gary D Bader,et al. Global Mapping of the Yeast Genetic Interaction Network , 2004, Science.
[35] F. Holstege,et al. A high resolution protein interaction map of the yeast Mediator complex. , 2004, Nucleic acids research.
[36] William Stafford Noble,et al. Kernel methods for predicting protein-protein interactions , 2005, ISMB.
[37] Manuel Ares,et al. Functional Cus1p Is Found with Hsh155p in a Multiprotein Splicing Factor Associated with U2 snRNA , 2000, Molecular and Cellular Biology.
[38] Sean R. Collins,et al. Toward a Comprehensive Atlas of the Physical Interactome of Saccharomyces cerevisiae*S , 2007, Molecular & Cellular Proteomics.
[39] Nello Cristianini,et al. A statistical framework for genomic data fusion , 2004, Bioinform..
[40] D. Botstein,et al. Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.
[41] William Stafford Noble,et al. Learning to predict protein-protein interactions from protein sequences , 2003, Bioinform..
[42] John D. Lafferty,et al. Diffusion Kernels on Graphs and Other Discrete Input Spaces , 2002, ICML.
[43] Jason Weston,et al. Gene functional classification from heterogeneous data , 2001, RECOMB.
[44] J D Beggs,et al. Genetic and physical interactions between factors involved in both cell cycle progression and pre-mRNA splicing in Saccharomyces cerevisiae. , 2000, Genetics.
[45] Douglas L. Brutlag,et al. Enumerating and Ranking Discrete Motifs , 1997, ISMB.
[46] Eleazar Eskin,et al. The Spectrum Kernel: A String Kernel for SVM Protein Classification , 2001, Pacific Symposium on Biocomputing.
[47] P. Brown,et al. Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.
[48] Jean-Loup Faulon,et al. Predicting protein-protein interactions using signature products , 2005, Bioinform..
[49] R. Durbin,et al. Pfam: A comprehensive database of protein domain families based on seed alignments , 1997, Proteins.
[50] Bernhard Schölkopf,et al. Support Vector Machine Applications in Computational Biology , 2004 .
[51] Bernhard Schölkopf,et al. Kernel Methods in Computational Biology , 2005 .
[52] E. Sprinzak,et al. Correlated sequence-signatures as markers of protein-protein interaction. , 2001, Journal of molecular biology.
[53] P. Novick,et al. Exo84p Is an Exocyst Protein Essential for Secretion* , 1999, The Journal of Biological Chemistry.
[54] 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.
[55] Dmitrij Frishman,et al. MIPS: a database for genomes and protein sequences , 1999, Nucleic Acids Res..
[56] Nello Cristianini,et al. Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast , 2003, Pacific Symposium on Biocomputing.
[57] T. Hughes,et al. High-definition macromolecular composition of yeast RNA-processing complexes. , 2004, Molecular cell.
[58] William Stafford Noble,et al. Choosing negative examples for the prediction of protein-protein interactions , 2006, BMC Bioinformatics.
[59] Ian M. Donaldson,et al. BIND: the Biomolecular Interaction Network Database , 2001, Nucleic Acids Res..