Computer Aided Knowledge Discovery in Biomedicine

Computer Aided Knowledge Discovery in Biomedicine

Vanathi Gopalakrishnan (University of Pittsburgh, USA)
Copyright: © 2012 |Pages: 15
DOI: 10.4018/978-1-60960-818-7.ch512
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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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Background

Knowledge discovery in biomedicine in the current world is very often the result of computational analyses combined with interpretation by domain experts. Langley (1998) states that artificial intelligence researchers have tried to develop intelligent artifacts that replicate the act of discovery. There are distinct steps in the scientific discovery process discussed therein (Langley, 1998) during which developers or users can influence the behavior of a computational discovery system. Furthermore, Langley (1998) suggests that such intervention is the preferred approach for using discovery software. In this chapter, we present an approach to data modeling and discovery that is consistent with this viewpoint.

Jurisica and Wigle (2006) define knowledge discovery (KD) as the process of extracting novel, useful, understandable and usable information from large data sets. The authors review knowledge discovery in proteomics and present examples of such algorithms in the literature that aid protein crystallization. The case studies presented in this chapter reflect state-of-the-art challenges in proteomics along with computer-aided solutions. Quantitative and qualitative discoveries are described along with the methods by which they are arrived at. The KD process in complex real-world domains requires multi-disciplinary methods involving both artificial intelligence and statistics applied to databases (Jurisica & Wigle, 2006).

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