Improved Algorithms For Discovery Of Transcription Factor Binding Sites In Dna Sequences
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Improved Algorithms for Discovery of Transcription Factor Binding Sites in Dna Sequences
Understanding the mechanisms that regulate gene expression is a major challenge in biology. One of the most important tasks in this challenge is to identify the transcription factors binding sites (TFBS) in DNA sequences. The common representation of these binding sites is called "motif" and the discovery of TFBS problem is also referred as motif finding problem in computer science. Despite extensive efforts in the past decade, none of the existing algorithms perform very well. This dissertation focuses on this difficult problem and proposes three new methods (MotifEnumerator, PosMotif, and Enrich) with excellent improvements. An improved pattern-driven algorithm, MotifEnumerator, is first proposed to detect the optimal motif with reduced time complexity compared to the traditional exact pattern-driven approaches. This strategy is further extended to allow arbitrary don̕ t care positions within a motif without much decrease in solvable values of motif length. The performance of this algorithm is comparable to the best existing motif finding algorithms on a large benchmark set of samples. Another algorithm with further post processing, PosMotif, is proposed to use a string representation that allows arbitrary ignored positions within the non-conserved portion of single motifs, and use Markov chains to model the background distributions of motifs of certain length while skipping these positions within each Markov chain. Two post processing steps considering redundancy information are applied in this algorithm. PosMotif demonstrates an improved performance compared to the best five existing motif finding algorithms on several large benchmark sets of samples. The third method, Enrich, is proposed to improve the performance of general motif finding algorithms by adding more sequences to the samples in the existing benchmark datasets. Five famous motif finding algorithms have been chosen to run on the original datasets and the enriched datasets, and the performance comparisons show a general great improvement on the enriched datasets.
Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013
Author: Zhixiang Yin
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-10-22
International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) is one of the flagship conferences on Bio-Computing, bringing together the world’s leading scientists from different areas of Natural Computing. Since 2006, the conferences have taken place at Wuhan (2006), Zhengzhou (2007), Adelaide (2008), Beijing (2009), Liverpool & Changsha (2010), Malaysia (2011) and India (2012). Following the successes of previous events, the 8th conference is organized and hosted by Anhui University of Science and Technology in China. This conference aims to provide a high-level international forum that researchers with different backgrounds and who are working in the related areas can use to present their latest results and exchange ideas. Additionally, the growing trend in Emergent Systems has resulted in the inclusion of two other closely related fields in the BIC-TA 2013 event, namely Complex Systems and Computational Neuroscience. These proceedings are intended for researchers in the fields of Membrane Computing, Evolutionary Computing and Genetic Algorithms, DNA and Molecular Computing, Biological Computing, Swarm Intelligence, Autonomy-Oriented Computing, Cellular and Molecular Automata, Complex Systems, etc. Professor Zhixiang Yin is the Dean of the School of Science, Anhui University of Science & Technology, China. Professor Linqiang Pan is the head of the research group of Natural Computing at Huazhong University of Science and Technology, Wuhan, China. Professor Xianwen Fang also works at the Anhui University of Science & Technology.
Advances in Neuro-Information Processing
The two volume set LNCS 5506 and LNCS 5507 constitutes the thoroughly refereed post-conference proceedings of the 15th International Conference on Neural Information Processing, ICONIP 2008, held in Auckland, New Zealand, in November 2008. The 260 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. 116 papers are published in the first volume and 112 in the second volume. The contributions deal with topics in the areas of data mining methods for cybersecurity, computational models and their applications to machine learning and pattern recognition, lifelong incremental learning for intelligent systems, application of intelligent methods in ecological informatics, pattern recognition from real-world information by svm and other sophisticated techniques, dynamics of neural networks, recent advances in brain-inspired technologies for robotics, neural information processing in cooperative multi-robot systems.