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

CHAN, Tak-Ming

 
9783639362794: Pattern Discovery for Deciphering Gene Regulation: with Evolutionary Computation and Data Mining Based Methods
Couverture souple
ISBN 10 : 3639362799 ISBN 13 : 9783639362794
Editeur : VDM Verlag Dr. Müller, 2011

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag Dr. Müller
Date d'édition
2011
Langue
anglais
ISBN 10 
3639362799
ISBN 13 
9783639362794
Reliure
Broché
Nombre de pages
144
Coordonnées du fabricant
non disponible
Personne responsable
non disponible