about
Analysis of VEGF--a regulated gene expression in endothelial cells to identify genes linked to angiogenesisBiomedical discovery acceleration, with applications to craniofacial developmentNetwork-based prediction of protein functionData Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and ImplicationsMethods for biological data integration: perspectives and challengesA critical assessment of Mus musculus gene function prediction using integrated genomic evidence.Multiple-platform data integration method with application to combined analysis of microarray and proteomic dataConsensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cellsPredicting drug-target interactions using drug-drug interactionsLearning from Heterogeneous Data Sources: An Application in Spatial ProteomicsA computational procedure for functional characterization of potential marker genes from molecular data: Alzheimer's as a case studyProtein interaction sentence detection using multiple semantic kernelsData integration in genetics and genomics: methods and challengesMachine learning for in silico virtual screening and chemical genomics: new strategiesAutomatic diagnosis of pathological myopia from heterogeneous biomedical dataIntegrative approaches to the prediction of protein functions based on the feature selection.iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data.Integrating multiple networks for protein function predictionBi-level multi-source learning for heterogeneous block-wise missing data.Bayesian methods for expression-based integration of various types of genomics data.A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: application to schizophrenia.Discovering disease-disease associations by fusing systems-level molecular dataMatrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.Bayesian joint analysis of heterogeneous genomics data.Protein fold recognition using geometric kernel data fusion.Improving clustering with metabolic pathway data.Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.Kernel-PCA data integration with enhanced interpretability.Kernel methods for large-scale genomic data analysis.Integration of molecular network data reconstructs Gene OntologySubtyping of Gliomaby Combining Gene Expression and CNVs Data Based on a Compressive Sensing Approach.Methods of integrating data to uncover genotype-phenotype interactions.Gene Prioritization by Compressive Data Fusion and ChainingFuse: multiple network alignment via data fusion.Inference of protein-protein interaction networks from multiple heterogeneous dataOptimized approach to decision fusion of heterogeneous data for breast cancer diagnosisPredicting disease trait with genomic data: a composite kernel approach.Probabilistic protein function prediction from heterogeneous genome-wide data.Simple integrative preprocessing preserves what is shared in data sourcesPredicting co-complexed protein pairs from heterogeneous data.
P2860
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P2860
description
2004 nî lūn-bûn
@nan
2004 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի մայիսին հրատարակված գիտական հոդված
@hy
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
name
A statistical framework for genomic data fusion.
@ast
A statistical framework for genomic data fusion.
@en
type
label
A statistical framework for genomic data fusion.
@ast
A statistical framework for genomic data fusion.
@en
prefLabel
A statistical framework for genomic data fusion.
@ast
A statistical framework for genomic data fusion.
@en
P2093
P356
P1433
P1476
A statistical framework for genomic data fusion.
@en
P2093
Gert R G Lanckriet
Nello Cristianini
Tijl De Bie
P304
P356
10.1093/BIOINFORMATICS/BTH294
P407
P577
2004-05-06T00:00:00Z