Mining complex genotypic features for predicting HIV-1 drug resistance.
BACKGROUND
Human immunodeficiency virus type 1 (HIV-1) evolves in human body, and its exposure to a drug often causes mutations that enhance the resistance against the drug. To design an effective pharmacotherapy for an individual patient, it is important to accurately predict the drug resistance based on genotype data. Notably, the resistance is not just the simple sum of the effects of all mutations. Structural biological studies suggest that the association of mutations is crucial: even if mutations A or B alone do not affect the resistance, a significant change might happen when the two mutations occur together. Linear regression methods cannot take the associations into account, while decision tree methods can reveal only limited associations. Kernel methods and neural networks implicitly use all possible associations for prediction, but cannot select salient associations explicitly.


RESULTS
Our method, itemset boosting, performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation combination is found by an efficient branch-and-bound search. This method uses all possible combinations, and salient associations are explicitly shown. In experiments, our method worked particularly well for predicting the resistance of nucleotide reverse transcriptase inhibitors (NRTIs). Furthermore, it successfully recovered many mutation associations known in biological literature.


BACKGROUND
http://www.kyb.mpg.de/bs/people/hiroto/iboost/.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Saigo
Hiroto
H
Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany.
Uno
Takeaki
T
Tsuda
Koji
K
D000465:Algorithms; D000998:Antiviral Agents / Q000008:administration & dosage*; D001483:Base Sequence; D004252:DNA Mutational Analysis / Q000379:methods*; D030541:Databases, Genetic; D024882:Drug Resistance, Viral / Q000235:genetics*; D005838:Genotype; D015497:HIV-1 / Q000145:classification / Q000187:drug effects* / Q000235:genetics*; D016247:Information Storage and Retrieval / Q000379:methods*; D008969:Molecular Sequence Data
D000998:Antiviral Agents