title: Hidden Markov Models, grammars, and biology: a tutorial.
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.
authors.affiliation: Association for Studies in Computational Biology, Kolkata 700 018, India. mshibaji@acm.org
authors.forename: Shibaji
authors.initials: S
authors.lastname: Mukherjee
authors.forename: Sushmita
authors.initials: S
authors.lastname: Mitra
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008390:Markov Chains; D008956:Models, Chemical; D008957:Models, Genetic; D008958:Models, Molecular; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*; D049490:Systems Biology / Q000379:methods