Proceedings of the 6th Asia-Pacific Bioinformatics Conference
Author | : Alvis Brazma |
Publisher | : Imperial College Press |
Total Pages | : 413 |
Release | : 2008 |
ISBN-10 | : 9781848161092 |
ISBN-13 | : 1848161093 |
Rating | : 4/5 (92 Downloads) |
Book excerpt: High-throughput sequencing and functional genomics technologies have given us the human genome sequence as well as those of other experimentally, medically, and agriculturally important species, thus enabling large-scale genotyping and gene expression profiling of human populations. Databases containing large numbers of sequences, polymorphisms, structures, metabolic pathways, and gene expression profiles of normal and diseased tissues are rapidly being generated for human and model organisms. Bioinformatics is therefore gaining importance in the annotation of genomic sequences; the understanding of the interplay among and between genes and proteins; the analysis of the genetic variability of species; the identification of pharmacological targets; and the inference of evolutionary origins, mechanisms, and relationships. This proceedings volume contains an up-to-date exchange of knowledge, ideas, and solutions to conceptual and practical issues of bioinformatics by researchers, professionals, and industry practitioners at the 6th Asia-Pacific Bioinformatics Conference held in Kyoto, Japan, in January 2008. Sample Chapter(s). Chapter 1: Recent Progress in Phylogenetic Combinatorics (185 KB). Contents: Recent Progress in Phylogenetic Combinatorics (A Dress); Predicting Nucleolar Proteins Using Support-Vector Machines (M Bod(r)n); Structure-Approximating Design of Stable Proteins in 2D HP Model Fortified by Cysteine Monomers (A H Khodabakhshi et al.); Seed Optimization Is No Easier than Optimal Golomb Ruler Design (B Ma & H Yao); Analysis of Structural Strand Asymmetry in Non-coding RNAs (J Wen et al.); Genome Halving with Double Cut and Join (R Warren & D Sankoff); Symbolic Approaches for Finding Control Strategies in Boolean Networks (C J Langmead & S K Jha); Optimal Algorithm for Finding DNA Motifs with Nucleotide Adjacent Dependency (F Y L Chin et al.); and other papers. Readership: Academics, researchers, and graduate students in bioinformatics and computer science.