Identifying differentially expressed genes in cancer patients using a non-parameter Ising model-Reference-Cited by-同舟云学术

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Identifying differentially expressed genes in cancer patients using a non-parameter Ising model

Published:2011-08-23 Issue:19 Volume:11 Page:3845-3852
ISSN:1615-9853
Container-title:PROTEOMICS
language:en
Short-container-title:Proteomics

Author:

Li Xumeng,Feltus Frank A.,Sun Xiaoqian,Wang James Z.,Luo Feng

Funder

Institute for Modeling and Simulation Applications at Clemson University

NSF funding

Publisher

Wiley

Subject

Molecular Biology,Biochemistry

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/pmic.201100180/fullpdf

Reference42 articles.

1. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes;Baldi;Bioinformatics,2001

2. Identifying differentially expressed genes using false discovery rate controlling procedures;Reiner;Bioinformatics,2003

3. Statistical significance for genome-wide studies;Storey;Proc. Natl. Acad. Sci. USA,2003

4. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles;Subramanian;Proc. Natl. Acad. Sci. USA,2005

5. Network-based classification of breast cancer metastasis;Chuang;Mol. Syst. Biol.,2007

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2. A modified Ising model of Barabási–Albert network with gene-type spins;Journal of Mathematical Biology;2020-09

3. Pathway-Based Genomics Prediction using Generalized Elastic Net;PLOS Computational Biology;2016-03-09

4. Incorporating prior knowledge into Gene Network Study;Bioinformatics;2013-08-16

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