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A Poisson-based adaptive affinity propagation clustering for SAGE data

Paper ID Volume ID Publish Year Pages File Format Full-Text
15257 1397 2010 8 PDF Available
Title
A Poisson-based adaptive affinity propagation clustering for SAGE data
Abstract

Serial analysis of gene expression (SAGE) is a powerful tool to obtain gene expression profiles. Clustering analysis is a valuable technique for analyzing SAGE data. In this paper, we propose an adaptive clustering method for SAGE data analysis, namely, PoissonAPS. The method incorporates a novel clustering algorithm, Affinity Propagation (AP). While AP algorithm has demonstrated good performance on many different data sets, it also faces several limitations. PoissonAPS overcomes the limitations of AP using the clustering validation measure as a cost function of merging and splitting, and as a result, it can automatically cluster SAGE data without user-specified parameters. We evaluated PoissonAPS and compared its performance with other methods on several real life SAGE datasets. The experimental results show that PoissonAPS can produce meaningful and interpretable clusters for SAGE data.

Keywords
Poisson distribution; Serial analysis of gene expression; Affinity Propagation; Clustering
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A Poisson-based adaptive affinity propagation clustering for SAGE data
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 34, Issue 1, February 2010, Pages 63–70
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us