A Multi-strategy approach to informative gene identification from gene expression data

Download
  1. (PDF, 479 KB)
  2. Get@NRC: A Multi-strategy approach to informative gene identification from gene expression data (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1142/S0219720010004495
AuthorSearch for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for:
TypeArticle
Journal titleJournal of Bioinformatics and Computational Biology
Volume8
Issue1
Pages1938; # of pages: 20
SubjectGene expression data analysis; Multi-strategy learning; Data mining and knowledge discovery
AbstractAn unsupervised multi-strategy approach has been developed to identify informative genes from high throughput genomic data. Several statistical methods have been used in the field to identify differentially expressed genes. Since different methods generate different lists of genes, it is very challenging to determine the most reliable gene list and the appropriate method. This paper presents a multi-strategy method, in which a combination of several data analysis techniques are applied to a given dataset and a confidence measure is established to select genes from the gene lists generated by these techniques to form the core of our final selection. The remainder of the genes that form the peripheral region are subject to exclusion or inclusion into the final selection. This paper demonstrates this methodology through its application to an in-house cancer genomics dataset and a public dataset. The results indicate that our method provides more reliable list of genes, which are validated using biological knowledge, biological experiments and literature search. We further evaluated our multi-strategy method by consolidating two pairs of independent datasets, each pair is for the same disease, but generated by different labs using different platforms. The results showed that our method has produced far better results.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology; NRC Biotechnology Research Institute
Peer reviewedYes
NPARC number16435932
Export citationExport as RIS
Report a correctionReport a correction
Record identifierc2977845-7f81-4623-8ac2-c407ee06c4cd
Record created2010-11-25
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)