Literature DB >> 17674632

Co-clustering: a versatile tool for data analysis in biomedical informatics.

Sungroh Yoon1, Luca Benini, Giovanni De Micheli.   

Abstract

Co-clustering has not been much exploited in biomedical informatics, despite its success in other domains. Most of the previous applications were limited to analyzing gene expression data. We performed co-clustering analysis on other types of data and obtained promising results, as summarized in this paper.

Mesh：

Year:  2007        PMID： 17674632     DOI： 10.1109/titb.2007.897575

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN： 1089-7771


  4 in total

1.  A comparative analysis of biclustering algorithms for gene expression data.

Authors:  Kemal Eren; Mehmet Deveci; Onur Küçüktunç; Ümit V Çatalyürek
Journal:  Brief Bioinform       Date:  2012-07-06       Impact factor: 11.622

2.  Full text clustering and relationship network analysis of biomedical publications.

Authors:  Renchu Guan; Chen Yang; Maurizio Marchese; Yanchun Liang; Xiaohu Shi
Journal:  PLoS One       Date:  2014-09-24       Impact factor: 3.240

3.  Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering.

Authors:  Chuan Gao; Ian C McDowell; Shiwen Zhao; Christopher D Brown; Barbara E Engelhardt
Journal:  PLoS Comput Biol       Date:  2016-07-28       Impact factor: 4.475

4.  A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data.

Authors:  Aaditya V Rangan; Caroline C McGrouther; John Kelsoe; Nicholas Schork; Eli Stahl; Qian Zhu; Arjun Krishnan; Vicky Yao; Olga Troyanskaya; Seda Bilaloglu; Preeti Raghavan; Sarah Bergen; Anders Jureus; Mikael Landen
Journal:  PLoS Comput Biol       Date:  2018-05-14       Impact factor: 4.475
  4 in total

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