decision-gd-gradpg-in-bio-2002

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Current applications and recent advances in genomics and proteomics Genomics and Proteomics Engineering in Medicine and Biology presents a well-rounded, interdisciplinary discussion of a topic that is at the cutting edge of both molecular biology and bioengineering. Compiling contributions by established experts, this book highlights up-to-date applications of biomedical informatics, as well as advancements in genomics-proteomics areas. Structures and algorithms are used to analyze genomic data and develop computational solutions for pathological understanding. Topics discussed include: Qualitative knowledge models Interpreting micro-array data Gene regulation bioinformatics Methods to analyze micro-array Cancer behavior and radiation therapy Error-control codes and the genome Complex life science multi-database queries Computational protein analysis Tumor and tumor suppressor proteins interactions

The three-volume set LNCS 6838, LNAI 6839, and LNBI 6840 constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Intelligent Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. This volume contains 93 revised full papers, from a total of 281 presentations at the conference - carefully reviewed and selected from 832 initial submissions. The papers address all issues in Advanced Intelligent Computing, especially Methodologies and Applications, including theories, methodologies, and applications in science and technology. They include a range of techniques such as artificial intelligence, pattern recognition, evolutionary computing, informatics theories and applications, computational neuroscience and bioscience, soft computing, human computer interface issues, etc.

A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology. Mourad Elloumi, PhD, is Professor in Computer Science at the University of Tunis-El Manar, Tunisia. Dr. Elloumi is the author/co-author of more than 50 publications in international journals and conference proceedings related to Algorithmics, Computational Molecular Biology, and Knowledge Discovery and Data Mining. Costas S. Iliopoulos, PhD, is Professor of AlgorithmDesign at King's College London, UK. Dr. Iliopoulos co-authored over 300 peer-reviewed articles in pattern matching and combinatorics of strings. He serves on the editorial board of the Journal of Discrete Algorithms, Computer Mathematics & Combinatorial Computing, and System Biology & Biomedical Technologies. Jason T. L. Wang, PhD, is Professor of Computer Science at the New Jersey Institute of Technology, USA. Dr. Wang has published extensively on Data Mining and Computational Molecular Biology, and has been a member of program committees for over 200 conferences and workshops in these and related areas. Albert Y. Zomaya, PhD, is the Chair Professor of High Performance Computing & Networking in the School of Information Technologies, University of Sydney, Australia. Dr. Zomaya published more than 500 scientific papers and articles and is author, co-author or editor of more than 20 books. Dr. Zomaya is Fellow of AAAS, IEEE, and IET.

We study haplotype reconstruction under the Mendelian law of inheritance and the minimum recombination principleon pedigree data. We prove that the problem of finding a mini-mum-recombinant haplotype configuration (MRHC) is in general NP-hard. This is the first complexity result concerning the problem to our knowledge. An iterative algorithm based on blocks of consecutive resolved marker loci (called block-extension) is proposed. It is very efficient and can be used for large pedigrees with a large number of markers, especially for those data sets requiring few recombinants (or recombination events). A polynomial-time exact algorithm for haplotype reconstruction without recombinants is also presented. This algorithm first identifies all the necessary constraints based on the Mendelian law and the zero recombinant assumption, and represents them using a system of linear equations over the cyclic group Z2. By using a simple method based on Gaussian elimination, we could obtain all possible feasible haplotype configurations. We have tested the block-extension algorithm on simulated data generated on three pedigree structures. The results show that the algorithm performs very well on both multi-allelic and biallelic data, especially when the number of recombinants is small.

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