Name
Abstract | ||
|---|---|---|
Biological sequences and structures have been modelled using various machine learning techniques and abstract mathematical concepts. This article surveys methods using Hidden Markov Model and functional grammars for this purpose. We provide a formal introduction to Hidden Markov Model and grammars, stressing on a comprehensive mathematical description of the methods and their natural continuity. The basic algorithms and their application to analyzing biological sequences and modelling structures of bio-molecules like proteins and nucleic acids are discussed. A comparison of the different approaches is discussed, and possible areas of work and problems are highlighted. Related databases and softwares, available on the internet, are also mentioned. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1142/S0219720005001077 | J. Bioinformatics and Computational Biology |
Keywords | Field | DocType |
machine learning,computational biology,hidden markov model | Stochastic context-free grammar,Rule-based machine translation,Computer science,Markov chain,Systems biology,Theoretical computer science,Artificial intelligence,Hidden Markov model,Machine learning,The Internet | Journal |
Volume | Issue | ISSN |
3 | 2 | 0219-7200 |
Citations | PageRank | References |
10 | 0.72 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shibaji Mukherjee | 1 | 10 | 1.06 |
| Sushmita Mitra | 2 | 2474 | 163.56 |