A regularized method for selecting nested groups of relevant genes from microarray data

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Christine De MolAlessandro Verri

Abstract

Gene expression analysis aims at identifying the genes able to accurately predict biological parameters like, for example, disease subtyping or progression. While accurate prediction can be achieved by means of many different techniques, gene identification, due to gene correlation and the limited number of available samples, is a much more elusive problem. Small changes in the expression values often produce different gene lists, and solutions which are both sparse and stable are difficult to obtain. We propose a two-stage regularization method able to learn linear models characterized by a high prediction performance. By varying a suitable parameter these linear models allow to trade sparsity for the inclusion of correlated genes and to produce gene lists which are almost perfectly nested. Experimental results on synthetic and microarray data confirm the interesting properties of the proposed method and its potential as a starting point for further biological investigations.

References

Nov 10, 2010·BMC Bioinformatics·Giorgio GuzzettaCesare Furlanello
Jul 23, 2011·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Marco MuselliGiorgio Valentini
Jul 24, 2010·Journal of Biomedicine & Biotechnology·Paolo FardinLuigi Varesio
Sep 8, 2016·Microarrays·Margherita SquillarioAnnalisa Barla

Citations

Jun 28, 2002·Cancer Cell·Dinesh SinghWilliam R Sellers
Feb 26, 2004·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Mark R SegalBruce R Conklin
Jul 28, 2005·Journal of Biomedicine & Biotechnology·Debashis Ghosh, Arul M Chinnaiyan
Aug 28, 2007·Bioinformatics·Yvan SaeysPedro Larrañaga
Jun 20, 2008·Briefings in Bioinformatics·Shuangge Ma, Jian Huang

Related Concepts

Regression Analysis
Cdna Microarrays
Malignant Neoplasms
Tumor Cells, Cultured
Gene Expression Analysis
Oncogenes
MRNA Differential Display
Disease Progression

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