Paper Info
Title
A New Fuzzy ARTMAP Approach for Predicting Biological Activity of Potential HIV-1 Protease Inhibitors
Abstract
The Fuzzy ARTMAP with Relevance factor (FAMR) is a Fuzzy ARTMAP (FAM) neural architecture with the fol- lowing property: Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase. Using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the in- formation source. We focus on the prediction of biological activities of HIV- 1 protease inhibitory compounds, both known and novel, using a FAMR model. Our new approach consists of two stages: i) During the first stage, we use a genetic algo- rithm (GA) to optimize the relevances assigned to the train- ing data. This improves the generalization capability of the FAMR. ii) In the second stage we use the optimized rele- vances to train the FAMR. Finally, the trained FAMR is used to predict the biological activities of newly designed poten- tial HIV-1 protease inhibitors.
Year
DOI
Venue
2007
10.1109/BIBM.2007.14
BIBM
Keywords
Field
DocType
trained famr,biological activity,protease inhibitory compound,potential hiv-1 protease inhibitors,sample pair,relevance factor,training phase,famr model,fuzzy artmap,training pair,hiv-1 protease inhibitor,new fuzzy artmap approach,optimization,enzymes,genetic algorithm,microorganisms,learning artificial intelligence,molecular biophysics,genetic algorithms
Training set,Ranking,Computer science,Fuzzy logic,HIV-1 protease,Neural net architecture,Fuzzy neural nets,Artificial intelligence,Bioinformatics,Genetic algorithm,Machine learning
Conference
ISSN
ISBN
Citations 
2156-1125
0-7695-3031-1
2
PageRank 
References 
Authors
0.37
5
4
Name
Order
Citations
PageRank
Razvan Andonie111717.71
Levente Fabry-asztalos2203.21
Lukas Magill320.37
Sarah Abdul-wahid4212.56