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          Institute: MPI für Informatik     Collection: Computational Biology and Applied Algorithmics     Display Documents

ID: 536640.0, MPI für Informatik / Computational Biology and Applied Algorithmics
Dealing with sparse data in predicting outcomes of HIV combination therapies
Authors:Bogojeska, Jasmina; Bickel, Steffen; Altmann, André; Lengauer, Thomas
Date of Publication (YYYY-MM-DD):2010
Title of Journal:Bioinformatics
Issue / Number:17
Start Page:2085
End Page:2092
Copyright:Copyright © 2010 Oxford University Press
Review Status:Peer-review
Audience:Experts Only
Intended Educational Use:No
Abstract / Description:Motivation: As there exists no cure or vaccine for the infection with human
immunodeficiency virus (HIV), the standard approach to treating HIV patients is
to repeatedly administer different combinations of several antiretroviral
drugs. Because of the large number of possible drug combinations, manually
finding a successful regimen becomes practically impossible. This presents a
major challenge for HIV treatment. The application of machine learning methods
for predicting virological responses to potential therapies is a possible
approach to solving this problem. However, due to evolving trends in treating
HIV patients the available clinical datasets have a highly unbalanced
representation, which might negatively affect the usefulness of derived
statistical models.

Results: This article presents an approach that tackles the problem of
predicting virological response to combination therapies by learning a separate
logistic regression model for each therapy. The models are fitted by using not
only the data from the target therapy but also the information from similar
therapies. For this purpose, we introduce and evaluate two different measures
of therapy similarity. The models are also able to incorporate phenotypic
knowledge on the therapy outcomes through a Gaussian prior. With our approach
we balance the uneven therapy representation in the datasets and produce higher
quality models for therapies with very few training samples. According to the
results from the computational experiments our therapy similarity model
performs significantly better than training separate models for each therapy by
using solely their examples. Furthermore, the model's performance is as good as
an approach that encodes therapy information in the input feature space with
the advantage of delivering better results for therapies with very few training
Last Change of the Resource (YYYY-MM-DD):2011-01-19
External Publication Status:published
Document Type:Article
Communicated by:Thomas Lengauer
Affiliations:MPI für Informatik/Computational Biology and Applied Algorithmics
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