Title :
Bioinformatics: a knowledge engineering approach
Author_Institution :
Sch. of Bus., Auckland Univ. of Technol., New Zealand
Abstract :
The paper introduces the knowledge engineering (KE) approach for the modeling and the discovery of new knowledge in bioinformatics. This approach extends the machine learning approach with various rule extraction and other knowledge representation procedures. Examples of the KE approach, and especially of one of the recently developed techniques - evolving connectionist systems (ECOS), to challenging problems in bioinformatics are given, that include: DNA sequence analysis, microarray gene expression profiling, protein structure prediction, finding gene regulatory networks, medical prognostic systems, computational neurogenetic modeling.
Keywords :
DNA; biology computing; knowledge engineering; learning (artificial intelligence); neural nets; proteins; DNA sequence analysis; bioinformatics; computational neurogenetic modeling; evolving connectionist systems; gene regulatory networks; knowledge engineering; knowledge-based neural networks; machine learning; medical prognostic systems; microarray gene expression profiling; protein structure prediction; rule extraction; Bioinformatics; Computational modeling; Computer networks; DNA computing; Gene expression; Knowledge engineering; Knowledge representation; Machine learning; Protein engineering; Sequences;
Conference_Titel :
Intelligent Systems, 2004. Proceedings. 2004 2nd International IEEE Conference
Print_ISBN :
0-7803-8278-1
DOI :
10.1109/IS.2004.1344630
