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Technical Report Details

Date 10-5-2007
Number DISI-TR-07-04B
Title A Regularized Method for Selecting Nested Groups of Relevant Genes from Microarray Data
Authors Christine De Mol, Sofia Mosci, Magali Traskine, Alessandro Verri
Bibtex Entry @TECHREPORT{DeMol07, author = "De Mol, C. and Mosci, S. and Traskine, M. and Verri, A.", title =
E-mail mosci@disi.unige.it
Link ftp://ftp.disi.unige.it/person/MosciS/PAPERS/TR0704B.pdf
Abstract Gene expression analysis aims at identifying the genes able to accurately predict some biological parameter like, for example, disease subtyping or progression. While accurate prediction can often be achieved by means of many techniques, gene identification, due to 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 consistent two-stage regularization method able to learn linear models characterized by high prediction performance. By varying a regularization parameter the linear models trade sparsity for the inclusion of correlated genes and produce gene lists which are almost perfectly nested. Experimental results on benchmark microarray confirm the interesting properties of the proposed method and its potential as a starting point for further biological investigations.
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