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Abstract:
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.
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.
