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Clustering MicroRNAs from Sequence and Time-Series Expression

Authors:
Didem Olcer
Hasan Ogul

Keywords: microRNA expression; microRNA regulation; graphical model; data integration; time-series data analysis

Abstract:
Inferring co-operative actions of microRNAs is crucial for analyzing large-scale gene regulatory networks. We introduce here a probabilistic generative model to cluster microRNAs from their mature sequences and time-series expression profiles. Sequence model is defined over the distribution of k-mers, all possible k-length substrings from RNA alphabet. The expression model is built upon a spline-basis function over a Gaussian assumption. Two models are integrated to form a single likelihood. Cluster enrichment analysis has shown that the data integration over a Bayesian framework could improve the clustering ability and produce biologically more plausible patterns.

Pages: 61 to 64

Copyright: Copyright (c) IARIA, 2013

Publication date: March 24, 2013

Published in: conference

ISSN: 2308-4383

ISBN: 978-1-61208-260-8

Location: Lisbon, Portugal

Dates: from March 24, 2013 to March 29, 2013