Predicting co-complexed protein pairs from heterogeneous data.
about
Simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway componentsComputational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experimentsResolving the structure of interactomes with hierarchical agglomerative clusteringComparative analysis and assessment of M. tuberculosis H37Rv protein-protein interaction datasetsSpotlite: web application and augmented algorithms for predicting co-complexed proteins from affinity purification--mass spectrometry data.Tissue-aware data integration approach for the inference of pathway interactions in metazoan organismsExploiting amino acid composition for predicting protein-protein interactionsProbabilistic inference of biological networks via data integrationAn evolutionary and structural characterization of mammalian protein complex organization.Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learningLarge-scale prediction of protein-protein interactions from structures.Protein complex forming ability is favored over the features of interacting partners in determining the evolutionary rates of proteins in the yeast protein-protein interaction networks.Expanding the landscape of chromatin modification (CM)-related functional domains and genes in human.Efficient prediction of co-complexed proteins based on coevolutionSupervised maximum-likelihood weighting of composite protein networks for complex prediction.A complex-based reconstruction of the Saccharomyces cerevisiae interactomeUpdate of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequenceSystems biology-based identification of Mycobacterium tuberculosis persistence genes in mouse lungsPredicting protein complex in protein interaction network - a supervised learning based methodEvolutionary rate heterogeneity of core and attachment proteins in yeast protein complexes.Training set expansion: an approach to improving the reconstruction of biological networks from limited and uneven reliable interactionsMachine learning applications in genetics and genomics.Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions.Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach.Up-to-date catalogues of yeast protein complexes.
P2860
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P2860
Predicting co-complexed protein pairs from heterogeneous data.
description
2008 nî lūn-bûn
@nan
2008 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Predicting co-complexed protein pairs from heterogeneous data.
@ast
Predicting co-complexed protein pairs from heterogeneous data.
@en
type
label
Predicting co-complexed protein pairs from heterogeneous data.
@ast
Predicting co-complexed protein pairs from heterogeneous data.
@en
prefLabel
Predicting co-complexed protein pairs from heterogeneous data.
@ast
Predicting co-complexed protein pairs from heterogeneous data.
@en
P2860
P1476
Predicting co-complexed protein pairs from heterogeneous data.
@en
P2093
P2860
P304
P356
10.1371/JOURNAL.PCBI.1000054
P577
2008-04-18T00:00:00Z