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This article is part of the supplement: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010)

Open Access Research

SFSSClass: an integrated approach for miRNA based tumor classification

Ramkrishna Mitra1*, Sanghamitra Bandyopadhyay1, Ujjwal Maulik2 and Michael Q Zhang34

Author Affiliations

1 Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

2 Department of Computer Science and & Engineering, Jadavpur University, Kolkata, India

3 Watson School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA

4 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST, Tsinghua University, Beijing 100084, China

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BMC Bioinformatics 2010, 11(Suppl 1):S22  doi:10.1186/1471-2105-11-S1-S22

Published: 18 January 2010

Additional files

Additional file 1:

Appendix for "SFSSClass: An integrated approach for miRNA based tumor classification". The detailed information about cross validation result and chosen optimal parameters for both the USC and the proposed method are given in the figures s1 to S6. A complete list of all the miRNAs involved in different cancer types is provided in Table S1. The differential expression patterns of miRNAs in different tumor tissues along with a list of references (PubMed-indexed for MEDLINE or PMID) are also present in this table. The information is obtained by extensive literature search. Other relevant parameters that have been considered are location of the miRNAs at fragile sites and cancer associated genomic regions, epigenetic alteration of miRNA expression and abnormalities in miRNA processing target genes and proteins.

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Open Data

Additional file 2:

A brief description on various classifiers that have been used for classifying tumor samples.

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Open Data