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Pattern Discovery for Deciphering Gene Regulation: with Evolutionary Computation and Data Mining Based Methods - Couverture souple
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.
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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.
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Pattern Discovery for Deciphering Gene Regulation
Edité par
VDM Verlag Dr. Müller, 2011
ISBN 10 : 3639362799
ISBN 13 : 9783639362794
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: CHAN Tak-MingDr. 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 abo. N° de réf. du vendeur 4981031
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Pattern Discovery for Deciphering Gene Regulation: with Evolutionary Computation and Data Mining Based Methods
Edité par
VDM Verlag Dr. M�ller, 2011
ISBN 10 : 3639362799
ISBN 13 : 9783639362794
Ancien ou d'occasion
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Pattern Discovery for Deciphering Gene Regulation: with Evolutionary Computation and Data Mining Based Methods
Edité par
VDM Verlag Dr. M ller, 2011
ISBN 10 : 3639362799
ISBN 13 : 9783639362794
Ancien ou d'occasion
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