Pattern Discovery for Deciphering Gene Regulation: with Evolutionary Computation and Data Mining Based Methods

CHAN, Tak-Ming

ISBN 10: 3639362799 / ISBN 13: 9783639362794

Editore: VDM Verlag Dr. M�ller, 2011

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Titolo: Pattern Discovery for Deciphering Gene ...

Casa editrice: VDM Verlag Dr. M�ller

Data di pubblicazione: 2011

Legatura: Brossura

Condizione: Like New

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Riassunto:

Transcription Factor (TF) and Transcription Factor Binding Site (TFBS) bindings are fundamental protein-DNA interactions in transcriptional regulation. TFs and TFBSs are conserved to form patterns (motifs) due to their important roles for controlling gene expressions and finally affecting functions and appearances. Pattern discovery is thus important for deciphering gene regulation, which has tremendous impacts on the understanding of life, bio-engineering and therapeutic applications. This thesis contributes to pattern discovery involving TFBS motifs and TF-TFBS associated sequence patterns based on Evolutionary Computation (EC), especially Genetic Algorithms (GAs), which are promising for bioinformatics problems with huge and noisy search space. On TFBS motif discovery, three novel GA based algorithms are developed, namely GALF-P with focus on optimization, GALF-G for modeling, and GASMEN for spaced motifs. TF-TFBS associated sequence pattern (rule) discovery is further investigated for better deciphering protein-DNA interactions in regulation. We for the first time generalize previous exact TF-TFBS rules to approximate ones using a progressive approach.

L'autore:

Dr. Tak-Ming CHAN obtained his Ph.D from the Department of Computer Science & Engineering, the Chinese University of Hong Kong in 2010, and his B.Sc. in Computer science from Fudan University in 2006. His research is about bioinformatics, data mining and evolutionary computation. He has been awarded in several local IEEE & ACM competitions.