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What is Hidden Markov Models (HMMs)

Handbook of Research on Systems Biology Applications in Medicine
These are directed chain-structured probabilistic graphical models that are generative in nature. They assume that the data are generated by a particular model and compute the joint distribution, P(x, y) of the observation sequence x, and the hidden state sequence y.
Published in Chapter:
Computer Aided Knowledge Discovery in Biomedicine
Vanathi Gopalakrishnan (University of Pittsburgh, USA)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/978-1-60566-076-9.ch007
Abstract
This chapter provides a perspective on 3 important collaborative areas in systems biology research. These areas represent biological problems of clinical significance. The first area deals with macromolecular crystallization, which is a crucial step in protein structure determination. The second area deals with proteomic biomarker discovery from high-throughput mass spectral technologies; while the third area is protein structure prediction and complex fold recognition from sequence and prior knowledge of structure properties. For each area, successful case studies are revisited from the perspective of computer- aided knowledge discovery using machine learning and statistical methods. Information about protein sequence, structure, and function is slowly accumulating in standardized forms within databases. Methods are needed to maximize the use of this prior information for prediction and analysis purposes. This chapter provides insights into such methods by which available information in existing databases can be processed and combined with systems biology expertise to expedite biomedical discoveries.
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