A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft computing: integrating evolutionary, neural, and fuzzy systems
Soft computing: integrating evolutionary, neural, and fuzzy systems
Virtual Screening: An Alternative or Complement to High Throughput Screening
Virtual Screening: An Alternative or Complement to High Throughput Screening
Virtual Screening for Bioactive Molecules
Virtual Screening for Bioactive Molecules
Metric Rule Generation with Septic Shock Patient Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining multiple comprehensible classification rules using genetic programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A neuro-fuzzy approach to virtual screening in molecular bioinformatics
Fuzzy Sets and Systems
Interactive exploration of fuzzy clusters using Neighborgrams
Fuzzy Sets and Systems
A novel approach to design classifiers using genetic programming
IEEE Transactions on Evolutionary Computation
Input features' impact on fuzzy decision processes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Artificial Intelligence in Medicine
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Drug design has emerged as an application area of soft computing methodology. To find potential novel drugs, up to millions of molecules need to be virtually screened by algorithmic techniques using computational devices. Due to the high combinatorial number of molecules experimental costs need to be decreased by improvements of the computational virtual screening method. In this contribution an adaptive neuro-fuzzy system is applied to find interval rules by cutting the adapted trapezoid membership functions, that provide knowledge about the important class of bioactive molecules as candidates for potential drugs. However, the aim is not to classify all the molecular data, but to find a small region, described by a rule, with a high enrichment of bioactive molecules. This small number of molecules could then be screened in the laboratory, limiting the costs clearly. The generated rules performed mostly better than common similarity measures and decision tree rules. Another part of the work herein was to increase again the perfomance of the best found rules so far by using evolutionary optimization. A comparison of four different parameter settings for the fitness function is given. The results of the hybrid approach are mainly more performant than the neuro-fuzzy interval rules solely.