title: Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification.
abstract: BACKGROUND
  Antiretroviral therapy is essential for human immunodeficiency virus (HIV) infected patients to inhibit viral replication and therewith to slow progression of disease and prolong a patient's life. However, the high mutation rate of HIV can lead to a fast adaptation of the virus under drug pressure and thereby to the evolution of resistant variants. In turn, these variants will lead to the failure of antiretroviral treatment. Moreover, these mutations cannot only lead to resistance against single drugs, but also to cross-resistance, i.e., resistance against drugs that have not yet been applied.
  
  
  METHODS
  662 protease sequences and 715 reverse transcriptase sequences with complete resistance profiles were analyzed using machine learning techniques, namely binary relevance classifiers, classifier chains, and ensembles of classifier chains.
  
  
  RESULTS
  In our study, we applied multi-label classification models incorporating cross-resistance information to predict drug resistance for two of the major drug classes used in antiretroviral therapy for HIV-1, namely protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs). By means of multi-label learning, namely classifier chains (CCs) and ensembles of classifier chains (ECCs), we were able to improve overall prediction accuracy for all drugs compared to hitherto applied binary classification models.
  
  
  CONCLUSIONS
  The development of fast and precise models to predict drug resistance in HIV-1 is highly important to enable a highly effective personalized therapy. Cross-resistance information can be exploited to improve prediction accuracy of computational drug resistance models.
authors.affiliation: Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315 Germany ; University of Applied Science Weihenstephan-Triesdorf, Am Hofgarten 4, Freising, 85354 Germany.
authors.forename: Mona
authors.initials: M
authors.lastname: Riemenschneider
authors.affiliation: Department of Computer Science, University of Paderborn, Pohlweg 47, Paderborn, 33098 Germany.
authors.forename: Robin
authors.initials: R
authors.lastname: Senge
authors.affiliation: Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315 Germany ; Wissenschaftszentrum Weihenstephan, Technische Universität München, Alte Akademie 8, Freising, 85354 Germany ; University of Applied Science Weihenstephan-Triesdorf, Am Hofgarten 4, Freising, 85354 Germany.
authors.forename: Ursula
authors.initials: U
authors.lastname: Neumann
authors.affiliation: Department of Computer Science, University of Paderborn, Pohlweg 47, Paderborn, 33098 Germany.
authors.forename: Eyke
authors.initials: E
authors.lastname: Hüllermeier
authors.affiliation: Department of Bioinformatics, Straubing Center of Science, Petersgasse 18, Straubing, 94315 Germany ; Wissenschaftszentrum Weihenstephan, Technische Universität München, Alte Akademie 8, Freising, 85354 Germany ; University of Applied Science Weihenstephan-Triesdorf, Am Hofgarten 4, Freising, 85354 Germany.
authors.forename: Dominik
authors.initials: D
authors.lastname: Heider
keywords: HIV therapy; Infectious diseases; Machine learning; Retrovirus