Faculty Opinions recommendation of Bayesian method to predict individual SNP genotypes from gene expression data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.14267400.15779623 ◽

2012 ◽

Author(s):

François Cambien

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Bayesian Method ◽

Expression Data

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Bayesian method to predict individual SNP genotypes from gene expression data

Nature Genetics ◽

10.1038/ng.2248 ◽

2012 ◽

Vol 44 (5) ◽

pp. 603-608 ◽

Author(s):

Eric E Schadt ◽

Sangsoon Woo ◽

Ke Hao

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Bayesian Method ◽

Expression Data

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Cancer Classification from Gene Expression data using Fuzzy-Rough techniques An Empirical Study

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i6.415420 ◽

2018 ◽

Vol 6 (6) ◽

pp. 415-420

Author(s):

Ansuman Kumar ◽

Anindya Halder

Keyword(s):

Gene Expression ◽

Empirical Study ◽

Gene Expression Data ◽

Cancer Classification ◽

Expression Data

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Statistical methods for analysis of time course gene expression data

Frontiers in Bioscience ◽

10.2741/a743 ◽

2002 ◽

Vol 7 (1) ◽

pp. a90-98 ◽

Author(s):

Hongzhe Li

Keyword(s):

Gene Expression ◽

Statistical Methods ◽

Gene Expression Data ◽

Time Course ◽

Expression Data

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Faculty Opinions recommendation of A new type of stochastic dependence revealed in gene expression data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1032265.370760 ◽

2006 ◽

Author(s):

Arcady Mushegian

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Expression Data ◽

Stochastic Dependence ◽

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Faculty Opinions recommendation of A systematic comparison and evaluation of biclustering methods for gene expression data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1087930.540878 ◽

2007 ◽

Author(s):

Daniel Chamovitz

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Expression Data ◽

Systematic Comparison

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Faculty Opinions recommendation of CAERUS: predicting CAncER oUtcomeS using relationship between protein structural information, protein networks, gene expression data, and mutation data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.11029956.11977055 ◽

2011 ◽

Author(s):

Yuanpeng Janet Huang

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Structural Information ◽

Protein Networks ◽

Expression Data ◽

Cancer Outcomes ◽

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Faculty Opinions recommendation of Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1116793.572870 ◽

2008 ◽

Author(s):

Laura Haneline

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

In Silico ◽

Leukemia Stem Cells ◽

Expression Data

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Faculty Opinions recommendation of Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1116793.1165055 ◽

2010 ◽

Author(s):

Anthony D Ho ◽

Eike C Buss ◽

Patrick Wuchter

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

In Silico ◽

Leukemia Stem Cells ◽

Expression Data

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Faculty Opinions recommendation of Pathway network inference from gene expression data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.723349104.793528476 ◽

2017 ◽

Author(s):

Lauren McIntyre

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Network Inference ◽

Expression Data ◽

Pathway Network

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Faculty Opinions recommendation of Meta-Analysis of Genome-Wide Association Studies and Network Analysis-Based Integration with Gene Expression Data Identify New Suggestive Loci and Unravel a Wnt-Centric Network Associated with Dupuytren's Disease.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726582766.793522025 ◽

2016 ◽

Author(s):

Rik Lories

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Gene Expression Data ◽

Association Studies ◽

Meta Analysis ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Expression Data ◽

Dupuytren's Disease ◽

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