Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction.
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A census of human soluble protein complexesKUPS: constructing datasets of interacting and non-interacting protein pairs with associated attributionsHigh-quality binary protein interaction map of the yeast interactome networkFiltering high-throughput protein-protein interaction data using a combination of genomic featuresRefining protein subcellular localizationComputational discovery of Epstein-Barr virus targeted human genes and signalling pathways.Novel roles for selected genes in meiotic DNA processing.Computational prediction of protein-protein interactions in Leishmania predicted proteomesLasting impressions: motifs in protein-protein maps may provide footprints of evolutionary eventsDiscriminative local subspaces in gene expression data for effective gene function prediction.Finding function: evaluation methods for functional genomic data.LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservationA novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.Accounting for redundancy when integrating gene interaction databasesProtein complex detection in PPI networks based on data integration and supervised learning method.Untargeted analysis of mass spectrometry data for elucidation of metabolites and function of enzymes.Predicting co-complexed protein pairs from heterogeneous data.Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations.Choosing negative examples for the prediction of protein-protein interactions.Genome-wide inference of protein interaction sites: lessons from the yeast high-quality negative protein-protein interaction dataset.Modeling ChIP sequencing in silico with applicationsFinding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting.The Symbiosis Interactome: a computational approach reveals novel components, functional interactions and modules in Sinorhizobium melilotiPredicting protein functions by relaxation labelling protein interaction network.Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learningDefining elastic fiber interactions by molecular fishing: an affinity purification and mass spectrometry approach.Integrative systems biology for data-driven knowledge discovery.Machine learning and genome annotation: a match meant to be?The impact of multifunctional genes on "guilt by association" analysisQiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards.Simplified method to predict mutual interactions of human transcription factors based on their primary structure.AIGO: towards a unified framework for the analysis and the inter-comparison of GO functional annotations.Is newer better?--evaluating the effects of data curation on integrated analyses in Saccharomyces cerevisiae.Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates.AdaBoost based multi-instance transfer learning for predicting proteome-wide interactions between Salmonella and human proteinsCluster-based assessment of protein-protein interaction confidenceNonspecific binding limits the number of proteins in a cell and shapes their interaction networks.Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagationPhysical protein-protein interactions predicted from microarraysClustering algorithms for detecting functional modules in protein interaction networks.
P2860
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P2860
Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction.
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
2004年论文
@zh
2004年论文
@zh-cn
name
Analyzing protein function on ...... atives for network prediction.
@ast
Analyzing protein function on ...... atives for network prediction.
@en
type
label
Analyzing protein function on ...... atives for network prediction.
@ast
Analyzing protein function on ...... atives for network prediction.
@en
prefLabel
Analyzing protein function on ...... atives for network prediction.
@ast
Analyzing protein function on ...... atives for network prediction.
@en
P1476
Analyzing protein function on ...... atives for network prediction.
@en
P2093
Mark Gerstein
Ronald Jansen
P304
P356
10.1016/J.MIB.2004.08.012
P577
2004-10-01T00:00:00Z