Bayesian correlated clustering to integrate multiple datasets.
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Cancer classification in the genomic era: five contemporary problemsIntegrative analyses of cancer data: a review from a statistical perspectiveStatistical Methods in Integrative GenomicsPredicting chemoinsensitivity in breast cancer with 'omics/digital pathology data fusionA Standardised Vocabulary for Identifying Benthic Biota and Substrata from Underwater Imagery: The CATAMI Classification Scheme.Similarity network fusion for aggregating data types on a genomic scale.Methods of integrating data to uncover genotype-phenotype interactions.DGEclust: differential expression analysis of clustered count data.Integrating heterogeneous genomic data to accurately identify disease subtypesMethods for the integration of multi-omics data: mathematical aspectsIntegrative clustering of high-dimensional data with joint and individual clusters.A graph theoretical approach to data fusionFlexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer.Machine learning and systems genomics approaches for multi-omics data.More Is Better: Recent Progress in Multi-Omics Data Integration MethodsIntegrated genomic analysis of biological gene sets with applications in lung cancer prognosisIntegration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes.Integrative analysis of multiple diverse omics datasets by sparse group multitask regression.A p-Median approach for predicting drug response in tumour cells.Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.Discovering study-specific gene regulatory networks.MDI-GPU: accelerating integrative modelling for genomic-scale data using GP-GPU computing.Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithmApproaches to uncovering cancer diagnostic and prognostic molecular signaturesBayesian consensus clusteringMultidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease.A journey to uncharted territory: new technical frontiers in studying tumor-stromal cell interactions.Pseudotime estimation: deconfounding single cell time seriesMulti-task consensus clustering of genome-wide transcriptomes from related biological conditions.Systems medicine of inflammaging.Integrating Gene Regulatory Networks to identify cancer-specific genes.Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.A novel approach for data integration and disease subtyping.Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).A computational framework for complex disease stratification from multiple large-scale datasets.Multi-omic and multi-view clustering algorithms: review and cancer benchmark
P2860
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P2860
Bayesian correlated clustering to integrate multiple datasets.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
2012年论文
@zh
2012年论文
@zh-cn
name
Bayesian correlated clustering to integrate multiple datasets.
@en
Bayesian correlated clustering to integrate multiple datasets.
@nl
type
label
Bayesian correlated clustering to integrate multiple datasets.
@en
Bayesian correlated clustering to integrate multiple datasets.
@nl
prefLabel
Bayesian correlated clustering to integrate multiple datasets.
@en
Bayesian correlated clustering to integrate multiple datasets.
@nl
P2860
P50
P356
P1433
P1476
Bayesian correlated clustering to integrate multiple datasets.
@en
P2093
David L Wild
Richard S Savage
P2860
P304
P356
10.1093/BIOINFORMATICS/BTS595
P407
P577
2012-10-09T00:00:00Z