Knowledge engineering for protein structure and motifs: design of a prototype system

• Published in: Fourth International Conference on Software Engineering and Knowledge Engineering : proceedings pp. 420 - 434
• Main Authors: Subramaniam, S., Tcheng, D.K., Kexiang Hu, Ragavan, H., Rendell, L.A.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE Comput. Soc. Press 1992
• Subjects: Biological system modeling; Induction generators; Knowledge engineering; Learning systems; Protein engineering
• ISBN: 9780818628306; 0818628308
• DOI: 10.1109/SEKE.1992.227960

A knowledge base and learning system is designed to help biologists predict protein structure and function based on sequence information. The knowledge base contains diverse information about: (a) protein motif sequences and structures, (b) heuristics and programs for identifying protein features, and (c) molecular simulation programs. The learning system selects the most relevant information for a particular prediction task and optimally integrates the information to generate accurate and comprehensible hypotheses. Biologists define the objectives for learning such as accuracy and comprehensibility. To overcome the limitations of existing induction algorithms, techniques are developed for constructing new features based on the existing knowledge base. Optimization algorithms are used for determining the best combination of induction and feature construction strategies for a problem.< >