Bioinformatics: Sequence Alignment and Markov Models
โ Scribed by Kal Sharma
- Publisher
- McGraw-Hill Professional
- Year
- 2008
- Tongue
- English
- Leaves
- 337
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
GET FULLY UP-TO-DATE ON BIOINFORMATICS-THE TECHNOLOGY OF THE 21ST CENTURY
Bioinformatics showcases the latest developments in the field along with all the foundational information you'll need. It provides in-depth coverage of a wide range of autoimmune disorders and detailed analyses of suffix trees, plus late-breaking advances regarding biochips and genomes.
Featuring helpful gene-finding algorithms, Bioinformatics offers key information on sequence alignment, HMMs, HMM applications, protein secondary structure, microarray techniques, and drug discovery and development. Helpful diagrams accompany mathematical equations throughout, and exercises appear at the end of each chapter to facilitate self-evaluation.
This thorough, up-to-date resource features:
- Worked-out problems illustrating concepts and models
- End-of-chapter exercises for self-evaluation
- Material based on student feedback
- Illustrations that clarify difficult math problems
- A list of bioinformatics-related websites
Bioinformatics covers:
- Sequence representation and alignment
- Hidden Markov models
- Applications of HMMs
- Gene finding
- Protein secondary structure prediction
- Microarray techniques
- Drug discovery and development
- Internet resources and public domain databases
๐ SIMILAR VOLUMES
GET FULLY UP-TO-DATE ON BIOINFORMATICS-THE TECHNOLOGY OF THE 21ST CENTURY Bioinformatics showcases the latest developments in the field along with all the foundational information you'll need. It provides in-depth coverage of a wide range of autoimmune disorders and detailed analyses of s
Preliminaries -- Alignment of a pair of sequences -- Sequence representation and string algorithms -- Multiple-sequence alignment -- Hidden Markov models and applications -- Gene finding, protein secondary structure -- Biochips -- Electrophoretic techniques and finite speed of diffusion.
Foreword. 1. Prerequisites in probability calculus. 2. Information and the Kullback Distance. 3. Probabilistic Models and Learning. 4. EM Algorithm. 5. Alignment and Scoring. 6. Mixture Models and Profiles. 7. Markov Chains. 8. Learning of Markov Chains. 9. Markovian Models for DNA sequences.
Demonstrating that many useful resources, such as databases, can benefit most bioinformatics projects, the Handbook of Hidden Markov Models in Bioinformatics focuses on how to choose and use various methods and programs available for hidden Markov models (HMMs). The book begins with discussions on