Name
Abstract | ||
|---|---|---|
Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy.Three different machine learning techniques were used: generative-discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome.The combined prediction engine will be available from July 2008, see http://engine.euresist.org. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/bioinformatics/btn141 | ISMB |
Keywords | Field | DocType |
optimizing hiv therapy,better prediction,combined prediction engine,prediction engine,similar engine,clinical factor,failure therapy,therapy failure,anti-hiv therapy,genotypic information,combined engine,therapy outcome,machine learning,receiver operating characteristic curve,pharmacogenetics | Data mining,Pharmacogenetics,Receiver operating characteristic,Regression,Computer science,Artificial intelligence,Therapy Outcome,Bioinformatics,Machine learning | Conference |
Volume | Issue | ISSN |
24 | 13 | 1367-4811 |
Citations | PageRank | References |
19 | 1.34 | 4 |
Authors | ||
13 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michal Rosen-Zvi | 1 | 1443 | 131.65 |
| Andre Altmann | 2 | 20 | 1.72 |
| Mattia C. F. Prosperi | 3 | 99 | 22.97 |
| E. Aharoni | 4 | 165 | 10.78 |
| Hani Neuvirth | 5 | 37 | 4.50 |
| Anders Sönnerborg | 6 | 20 | 1.82 |
| Eugen Schülter | 7 | 24 | 2.13 |
| Daniel Struck | 8 | 20 | 1.68 |
| Yardena Peres | 9 | 24 | 3.23 |
| Francesca Incardona | 10 | 24 | 3.30 |
| Rolf Kaiser | 11 | 213 | 47.27 |
| Maurizio Zazzi | 12 | 55 | 6.77 |
| Thomas Lengauer | 13 | 3155 | 605.03 |