Bioinformatics: a knowledge engineering approach

dc.contributor.authorKasabov, N
dc.date.accessioned2009-05-27T22:18:54Z
dc.date.available2009-05-27T22:18:54Z
dc.date.copyright2004
dc.date.created2004
dc.date.issued2004
dc.description.abstractThe 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.
dc.identifier.urihttps://hdl.handle.net/10292/612
dc.publisherIEEE
dc.relation.urihttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1344630&isnumber=29614
dc.rights©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.rights.accessrightsOpenAccess
dc.source2nd International IEEE Conference on Intelligent Systems, Sofia, Bulgaria, 1, 19-24
dc.titleBioinformatics: a knowledge engineering approach
dc.typeConference Proceedings
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