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Abstract:
Infections with the human immunodeficiency virus type 1 (HIV-1) are treated
with combinations of drugs. Unfortunately, HIV responds to the treatment by
developing resistance mutations. Consequently, the genome of the viral target
proteins is sequenced and inspected for resistance mutations as part of routine
diagnostic procedures for ensuring an effective treatment. For predicting
response to a combination therapy, currently available computer-based methods
rely on the genotype of the virus and the composition of the regimen as input.
However, no available tool takes full advantage of the knowledge about the
order of and the response to previously prescribed regimens. The resulting
high-dimensional feature space makes existing methods difficult to apply in a
straightforward fashion. The machine learning system proposed in this work,
sequence boosting, is tailored to exploiting such high-dimensional information,
i.e. the extraction of longitudinal features, by utilizing the recent
advancements in data mining and boosting.
When applied to predicting the latest treatment outcome for 3,759
treatment-experienced patients from the EuResist integrated database, sequence
boosting achieved superior performance compared to SVMs with RBF kernels.
Moreover, sequence boosting allows an easy access to the discriminative
treatment information.
Analysis of feature importance values provided by our model confirmed known
facts regarding HIV treatment. For instance, application of potent and recently
licensed drugs was beneficial for patients, and, conversely, the patient group
that was subject to NRTI mono-therapies in the past had poor treatment
perspectives today. Furthermore, our model revealed novel biological insights.
More precisely, the combination of previously used drugs with their in vivo
response is more informative than the information of previously used drugs
alone. Using this information improves the performance of systems for
predicting therapy outcome.
