Learning gene functional classifications from multiple data types.
about
GeneMANIA: a real-time multiple association network integration algorithm for predicting gene functionThe FunCat, a functional annotation scheme for systematic classification of proteins from whole genomesPredicting 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 challengesCCR2 modulates inflammatory and metabolic effects of high-fat feedingPoGO: Prediction of Gene Ontology terms for fungal proteins.Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studiesAn integrated approach (CLuster Analysis Integration Method) to combine expression data and protein-protein interaction networks in agrigenomics: application on Arabidopsis thalianaIntegrating multiple networks for protein function predictionA Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).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.Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration.Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosisMicroarray data analysis: from disarray to consolidation and consensus.Support vector machines and kernels for computational biologyDirecting experimental biology: a case study in mitochondrial biogenesis.XML-based approaches for the integration of heterogeneous bio-molecular dataFast integration of heterogeneous data sources for predicting gene function with limited annotationSVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells.Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network.ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples.Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles.Large datasets in biomedicine: a discussion of salient analytic issues.Phyletic profiling with cliques of orthologs is enhanced by signatures of paralogy relationships.Physical protein-protein interactions predicted from microarraysUsing PPI network autocorrelation in hierarchical multi-label classification trees for gene function predictionAlzheimer's disease risk assessment using large-scale machine learning methodsThe characteristic direction: a geometrical approach to identify differentially expressed genes.Protein domain recurrence and order can enhance prediction of protein functions.Analysis of cascading failure in gene networksFunctional modules by relating protein interaction networks and gene expression.MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets.The zebrafish: scalable in vivo modeling for systems biology.Hierarchical ensemble methods for protein function prediction.Machine learning applications in genetics and genomics.Predicting gene function in a hierarchical context with an ensemble of classifiersHierarchical multi-label prediction of gene function.
P2860
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P2860
Learning gene functional classifications from multiple data types.
description
2002 nî lūn-bûn
@nan
2002 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2002 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2002年の論文
@ja
2002年論文
@yue
2002年論文
@zh-hant
2002年論文
@zh-hk
2002年論文
@zh-mo
2002年論文
@zh-tw
2002年论文
@wuu
name
Learning gene functional classifications from multiple data types.
@ast
Learning gene functional classifications from multiple data types.
@en
type
label
Learning gene functional classifications from multiple data types.
@ast
Learning gene functional classifications from multiple data types.
@en
prefLabel
Learning gene functional classifications from multiple data types.
@ast
Learning gene functional classifications from multiple data types.
@en
P50
P1476
Learning gene functional classifications from multiple data types.
@en
P2093
Jinsong Cai
P304
P356
10.1089/10665270252935539
P577
2002-01-01T00:00:00Z