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|a 9780387713373
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| 100 |
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|a Xia, Xuhua
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| 245 |
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|a Bioinformatics and the Cell
|h Elektronische Ressource
|b Modern Computational Approaches in Genomics, Proteomics and Transcriptomics
|c by Xuhua Xia
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| 250 |
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|a 1st ed. 2007
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| 260 |
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|a New York, NY
|b Springer US
|c 2007, 2007
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| 300 |
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|a XVI, 350 p
|b online resource
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| 505 |
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|a Blast and Fasta -- Sequence alignment -- Contig assembly -- DNA replication and viral evolution -- Gene and motif prediction -- Hidden Markov Models -- Gibbs Sampler -- Bioinformatics and vertebrate mitochondria -- Characterizing translation efficiency -- Protein isoelectric point -- Bioinformatics and Two-Dimensional Protein Separation -- Self-Organizing Map and other clustering Algorithms -- Molecular Phylogenetics -- Fundamentals of Proteomics
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| 653 |
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|a Cell Biology
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| 653 |
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|a Bioinformatics
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| 653 |
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|a Evolutionary Biology
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| 653 |
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|a Protein Biochemistry
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| 653 |
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|a Cytology
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| 653 |
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|a Evolution (Biology)
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| 653 |
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|a Biochemistry
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| 653 |
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|a Proteins
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
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|a 10.1007/978-0-387-71337-3
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| 856 |
4 |
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|u https://doi.org/10.1007/978-0-387-71337-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 570,285
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| 520 |
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|a A sample of the illustrated algorithms includes: String matching algorithms with associated statistics Sequence alignment with the affine function and profile alignment for multiple sequence alignment Contig assembly algorithms Perceptrons Hidden Markov models Gibbs sampler Self-organizing map Algorithms in molecular phylogenetics EM algorithm for maximum likelihood calculation Monte Carlo integration and Markov chain Monte Carlo algorithms for Bayesian inference Mass deconvolution and computational methods for peptide mass fingerprinting By rendering both mathematics and biology to numbers, this book should appeal not only to biologists, but also to mathematicians, biostatisticians, and computational scientists, especially those younger ones with an ambition to conquer the rough terrain of bioinformatics. Dr.
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| 520 |
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|a Xuhua Xia obtained his PhD in population biology at University of Western Ontario in 1990 and made his entry into molecular evolution and phylogenetics at University of Washington in 1993. He was recruited by University of Hong Kong in 1996 as an assistant professor, and served as a senior scientist and the founding head of the Bioinformatics Laboratory of HKU-Pasteur Research Centre. Dr. Xia joined University of Ottawa in 2002 where he is cross-appointed to both Biology Department and the School of Information Technology Engineering. He is an active member in the Center for Advanced Research in Environmental Genomics and the Ottawa Institute of Systems Biology. His web site is at http://dambe.bio.uottawa.ca
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| 520 |
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|a "Xuhua Xia’s Bioinformatics and the Cell is a welcome addition to the bourgeoning field of bioinformatics text books. Xia stakes out a too-often neglected middle ground in bioinformatics by presenting a work that emphasizes methods’ biological utility without eschewing algorithmic formalism. Readers will find a well-rounded presentation of bioinformatics techniques employed in genomics, transcriptomics and proteomics – unified throughout by the common theme of molecular evolution." - I. King Jordan, School of Biology, Georgia Institute of Technology "Bioinformatics and the Cell is a remarkable book because it integrates the more technical and mathematical aspects of bioinformatics with concrete examples of their application to current research problems in molecular and cellular biology. I believe that this broad, unified approach is made possible, in large part, by the very wide scope of Dr. Xia’s own research experience.
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| 520 |
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|a The integration of genomics, proteomics and transcriptomics into a single volume makes this book required reading for students entering the new and emerging field of Systems Biology. This is truly a path breaking book." - Donal Hickey, Concordia University, Montreal, Quebec, Canada Bioinformatics and the Cell is the first book with comprehensive numerical illustration of mathematical techniques and computational algorithms used in bioinformatics to convert the rapidly increasing molecular data into organized biological knowledge, with a focus on interactions among DNA, RNA and proteins in the cell.
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