Computational Biology And Genome Informatics
eBook - PDF

Computational Biology And Genome Informatics

  1. 268 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Computational Biology And Genome Informatics

About this book

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.

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Yes, you can access Computational Biology And Genome Informatics by Paul P Wang, Jason T L Wang, Cathy H Wu in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Processing. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Preface
  2. Contents
  3. Chapter 1 Exploring RNA Intermediate Conformations with the Massively Parallel Genetic Algorithm
  4. Chapter 2 Introduction to Self-Assembling DNA Nanostructures for Computation and Nanofabrication
  5. Chapter 3 Mapping Sequence to Rice FPC
  6. Chapter 4 Graph Theoretic Sequence Clustering Algorithms and Their Applications to Genome Comparison
  7. Chapter 5 The Protein Information Resource for Functional Genomics and Proteomics
  8. Chapter 6 High-Grade Ore for Data Mining in 3D Structures
  9. Chapter 7 Protein Classification: A Geometric Hashing Approach
  10. Chapter 8 Interrelated Clustering: An Approach for Gene Expression Data Analysis
  11. Chapter 9 Creating Metabolic Network Models using Text Mining and Expert Knowledge
  12. Chapter 10 Phyloinformatics and Tree Networks
  13. Index