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

Tak-Ming Chan

ISBN 10: 3639362799 / ISBN 13: 9783639362794

Published by VDM Verlag Dr. Müller, 2011

About this Item

Pattern Discovery for Deciphering Gene Regulation | with Evolutionary Computation and Data Mining Based Methods | Tak-Ming Chan | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639362794 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 106912113

Bibliographic Details

• Title: Pattern Discovery for Deciphering Gene Regulation | with Evolutionary Computation and Data Mining Based Methods
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2011
• Language: English
• ISBN 10: 3639362799
• ISBN 13: 9783639362794
• Binding: Taschenbuch
• Condition: Neu
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

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.