Chapter 11. Probabilistic Models for Long-Range Features in Biosequences

  1. Yan-Qing Zhang2 and
  2. Jagath C. Rajapakse3
  1. Li Liao

Published Online: 21 APR 2008

DOI: 10.1002/9780470397428.ch11

Machine Learning in Bioinformatics

Machine Learning in Bioinformatics

How to Cite

Liao, L. (2008) Probabilistic Models for Long-Range Features in Biosequences, in Machine Learning in Bioinformatics (eds Y.-Q. Zhang and J. C. Rajapakse), John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9780470397428.ch11

Editor Information

  1. 2

    Georgia State University, Atlanta, Georgia

  2. 3

    School of Computer Engineering, and The Bioinformatics Research Center, Nanyang Technological University, Nanyang, Singapore

Author Information

  1. University of Delaware, Newark, Delaware, USA

Publication History

  1. Published Online: 21 APR 2008
  2. Published Print: 12 NOV 2008

Book Series:

  1. Bioinformatics: Computational Techniques and Engineering

Book Series Editors:

  1. Professor Yi Pan and
  2. Professor Albert Y. Zomaya

ISBN Information

Print ISBN: 9780470116623

Online ISBN: 9780470397428

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Keywords:

  • probabilistic models capturing long-range features in biosequences;
  • computational approaches and challenges with long-range features;
  • long-range features in RNA requiring different models

Summary

This chapter contains sections titled:

  • DNA, RNA, and Proteins

  • General Tasks in Sequence Analysis

  • Computational Approaches and Challenges with Long-Range Features

  • Transmembrane Proteins

  • Long-Range Features in RNA

  • Summary

  • References