A SOFM approach to predicting HIV drug resistance.
The self-organizing feature map (SOFM or SOM) neural network approach has been applied to a number of life sciences problems. In this paper, we apply SOFMs in predicting the resistance of the HIV virus to Saquinavir, an approved protease inhibitor. We show that a SOFM predicts resistance to Saquinavir with reasonable success based solely on the amino acid sequence of the HIV protease mutation. The best single network provided 69% coverage and 68% accuracy. We then combine a number of networks into various majority voting schemes. All of the combinations showed improved performance over the best single network, with an average of 85% coverage and 78% accuracy. Future research objectives are suggested based on these results.
Potter
R Brian
RB
Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. potterb@ieee.org
Draghici
Sorin
S
D019943:Amino Acid Substitution; D019380:Anti-HIV Agents / Q000494:pharmacology; D024882:Drug Resistance, Viral / Q000235:genetics*; D006678:HIV / Q000187:drug effects / Q000235:genetics*; D016333:HIV Protease / Q000235:genetics*; D017320:HIV Protease Inhibitors / Q000494:pharmacology*; D006801:Humans; D009154:Mutation; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D019258:Saquinavir / Q000494:pharmacology
D019380:Anti-HIV Agents; D017320:HIV Protease Inhibitors; D016333:HIV Protease; D019258:Saquinavir