A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).
about
Analysis of VEGF--a regulated gene expression in endothelial cells to identify genes linked to angiogenesisBiomedical discovery acceleration, with applications to craniofacial developmentMachine learning helps identify CHRONO as a circadian clock componentIn silico gene prioritization by integrating multiple data sourcesThe interactome: Predicting the protein-protein interactions in cellsA large-scale evaluation of computational protein function predictionRevealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide dataGene Ontology annotations at SGD: new data sources and annotation methodsComprehensive curation and analysis of global interaction networks in Saccharomyces cerevisiaePredicting co-complexed protein pairs using genomic and proteomic data integrationAVID: an integrative framework for discovering functional relationships among proteinsProtein molecular function prediction by Bayesian phylogenomics.Methods for biological data integration: perspectives and challengesUsing biological networks to improve our understanding of infectious diseasesDiscovery of biological networks from diverse functional genomic data.A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.Assessing systems properties of yeast mitochondria through an interaction map of the organelleThe emerging era of genomic data integration for analyzing splice isoform functionThe clustering of functionally related genes contributes to CNV-mediated diseaseCoordinated concentration changes of transcripts and metabolites in Saccharomyces cerevisiaeTARGETgene: a tool for identification of potential therapeutic targets in cancerMining rare associations between biological ontologiesRole of SPI-1 secreted effectors in acute bovine response to Salmonella enterica Serovar Typhimurium: a systems biology analysis approachData integration in genetics and genomics: methods and challengesScoring protein relationships in functional interaction networks predicted from sequence dataAn attempt to construct a (general) mathematical framework to model biological "context-dependence".PoGO: Prediction of Gene Ontology terms for fungal proteins.Toward a "structural BLAST": using structural relationships to infer function.Integrative approaches to the prediction of protein functions based on the feature selection.Gene divergence and pathway duplication in the metabolic network of yeast and digital organisms.Novel cardiovascular gene functions revealed via systematic phenotype prediction in zebrafishChapter 2: Data-driven view of disease biologyMS-kNN: protein function prediction by integrating multiple data sourcesBi-level multi-source learning for heterogeneous block-wise missing data.Bayesian network prior: network analysis of biological data using external knowledge.Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.Finding function: evaluation methods for functional genomic data.Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources.Subtyping of Gliomaby Combining Gene Expression and CNVs Data Based on a Compressive Sensing Approach.Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms
P2860
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P2860
A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).
description
2003 nî lūn-bûn
@nan
2003 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
name
A Bayesian framework for combi ...... (in Saccharomyces cerevisiae).
@ast
A Bayesian framework for combi ...... (in Saccharomyces cerevisiae).
@en
type
label
A Bayesian framework for combi ...... (in Saccharomyces cerevisiae).
@ast
A Bayesian framework for combi ...... (in Saccharomyces cerevisiae).
@en
prefLabel
A Bayesian framework for combi ...... (in Saccharomyces cerevisiae).
@ast
A Bayesian framework for combi ...... (in Saccharomyces cerevisiae).
@en
P2860
P50
P356
P1476
A Bayesian framework for combi ...... (in Saccharomyces cerevisiae).
@en
P2093
Art B Owen
Kara Dolinski
P2860
P304
P356
10.1073/PNAS.0832373100
P407
P577
2003-06-25T00:00:00Z