Extraction of comprehensible logical rules from neural networks: application of TREPAN in bio and cheminformatics
Year: 2005
Type: article
Abstract: TREPAN is an algorithm for the extraction of comprehensible rules from trained neural networks. The method has been applied successfully to biological sequence (bioinformatics) problems. It has now been extended to handle chemoinformatics (QSAR) datasets. The method has been shown to have advantages over traditional symbolic rule induction methods such as C5. Results obtained for bioinformatics and chemoinformatics problems using the TREPAN algorithm are presented.
Source: Croatica Chemica Acta
Authors: Brian D. Hudson, David S. Whitley, Antony Browne, Martyn G. Ford
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W1930489587