An information-theoretic machine learning approach to expression QTL analysis.
about
Novel candidate key drivers in the integrative network of genes, microRNAs, methylations, and copy number variations in squamous cell lung carcinoma.Genetic differences among ethnic groups.Gene expression elucidates functional impact of polygenic risk for schizophrenia.Assessment of variation in immunosuppressive pathway genes reveals TGFBR2 to be associated with risk of clear cell ovarian cancer.Integrative multi-omics analysis revealed SNP-lncRNA-mRNA (SLM) networks in human peripheral blood mononuclear cells.Genome-wide integrative analysis identified SNP-miRNA-mRNA interaction networks in peripheral blood mononuclear cells.Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods.Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.Nonlinear quantitative radiation sensitivity prediction model based on NCI-60 cancer cell lines.Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy
P2860
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P2860
An information-theoretic machine learning approach to expression QTL analysis.
description
2013 nî lūn-bûn
@nan
2013 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
An information-theoretic machine learning approach to expression QTL analysis.
@ast
An information-theoretic machine learning approach to expression QTL analysis.
@en
An information-theoretic machine learning approach to expression QTL analysis.
@nl
type
label
An information-theoretic machine learning approach to expression QTL analysis.
@ast
An information-theoretic machine learning approach to expression QTL analysis.
@en
An information-theoretic machine learning approach to expression QTL analysis.
@nl
prefLabel
An information-theoretic machine learning approach to expression QTL analysis.
@ast
An information-theoretic machine learning approach to expression QTL analysis.
@en
An information-theoretic machine learning approach to expression QTL analysis.
@nl
P2860
P1433
P1476
An information-theoretic machine learning approach to expression QTL analysis.
@en
P2093
P2860
P304
P356
10.1371/JOURNAL.PONE.0067899
P407
P50
P577
2013-06-25T00:00:00Z