A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy.

ABSTRACT: Human Immunodeficiency Virus Type 1 (HIV-1) infection is associated with high mortality if no therapy is provided. Currently, the treatment of an HIV-1 positive patient requires that several drugs should be taken simultaneously. The resistance of the virus to an antiretroviral drug may lead to treatment failure. Our approach focuses on predicting the exposure of a particular viral variant to an antiretroviral drug or drug combination. It also aims at the prediction of drug treatment success or failure. We utilized nucleotide sequences of HIV-1 encoding protease and reverse transcriptase to perform such types of prediction. The PASS (Prediction of Activity Spectra for Substances) algorithm based on the naive Bayesian classifier was used to make a prediction. We calculated the probability of

SUBMITTER: Tarasova O 

PROVIDER: S-EPMC7037494 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature