计算生物学及基因信息学COMPUTATIONAL BIOLOGY AND GENOME INFORMATICS 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


计算生物学及基因信息学COMPUTATIONAL BIOLOGY AND GENOME INFORMATICS

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Wang, Paul P. 编 作者
World Scientific Pub Co Inc
译者
2003-12 出版日期
257 页数
358.00元 价格
HRD
丛书系列
9789812382573 图书编码

计算生物学及基因信息学COMPUTATIONAL BIOLOGY AND GENOME INFORMATICS 在线电子书 图书标签: 生物學  生物  生物-生物数学  专业书   


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计算生物学及基因信息学COMPUTATIONAL BIOLOGY AND GENOME INFORMATICS 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

计算生物学及基因信息学COMPUTATIONAL BIOLOGY AND GENOME INFORMATICS 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

计算生物学及基因信息学COMPUTATIONAL BIOLOGY AND GENOME INFORMATICS 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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计算生物学及基因信息学COMPUTATIONAL BIOLOGY AND GENOME INFORMATICS 在线电子书 图书描述

This book contains articles written by experts on a wide range of topics that are associated with the analysis and management of biological information at the molecular level. It contains chapters on RNA and protein structure analysis, DNA computing, sequence mapping, genome comparison, gene expression data mining, metabolic network modeling, and phyloinformatics. The important work of some representative researchers in bioinformatics is brought together for the first time in one volume. The topic is treated in depth and is related to, where applicable, other emerging technologies such as data mining and visualization. The goal of the book is to introduce readers to the principle techniques of bioinformatics in the hope that they will build on them to make new discoveries of their own. Contents: Exploring RNA Intermediate Conformations with the Massively Parallel Genetic Algorithm; Introduction to Self-Assembling DNA Nanostructures for Computation and Nanofabrication; Mapping Sequence to Rice FPC; Graph Theoretic Sequence Clustering Algorithms and their Applications to Genome Comparison; The Protein Information Resource for Functional Genomics and Proteomics; High-Grade Ore for Data Mining in 3D Structures; Protein Classification: A Geometric Hashing Approach; Interrelated Clustering: An Approach for Gene Expression Data Analysis; Creating Metabolic Network Models Using Text Mining and Expert Knowledge; Phyloinformatics and Tree Networks. Readership: Molecular biologists who rely on computers and mathematical scientists with interests in biology.

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