Predicting the Response to Combination Antiretroviral Therapy: Retrospective Validation of geno2pheno-THEO on a Large Clinical Database

BackgroundExpert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure MethodsWe retrospectively validated the statistical model used by g2p-THEO in ∼7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega ResultsThe difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P<.001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed ConclusionFinding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.or

Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy

BACKGROUND: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. PRINCIPAL FINDINGS: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. CONCLUSION: The combined EuResist prediction engine is freely available at http://engine.euresist.org

Xeena for schema: creating XML documents with a coordinated grammar tree

The vast heterogeneous network that is the World Wide Web requires common languages to facilitate the exchange and display of data and information in many forms. The Word Wide Web Consortium (W3C) developed the extensible markup language (XML) for this purpose. XML documents are produced automatically by applications or manually by users. When users do not produce documents regularly or when document languages are large and complex, manual editing can be a challenge. In these situations, better manual editing facilities that guide users and ease the burden of learning and recalling XML languages are needed. We present an XML editor design implemented in our Xeena for schema editor that addresses these needs. It is based on a new tree based grammar view that guides novice users and empowers experienced users to build XML documents. It lets users see and edit multiple levels of potential elements, unlike existing editors that present only one level of potential elements. We demonstrate its key features, present our grammar tree view design both informally and formally, and describe a user evaluation that supports the usability of our design

Selecting anti-HIV therapies based on a variety of genomic and clinical factors

Motivation: 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. Results: 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. Availability: The combined prediction engine will be available from July 2008, see http://engine.euresist.org Contact: [email protected]

Results for the individual classifiers on training set and test set.

<p>The table displays the performance, measured in AUC and Accuracy, achieved by the individual classifiers on the training set (using 10-fold cross validation; standard deviation in brackets) and the test set using different feature sets.</p

Summary of the EuResist Integrated Database (release 11/2007) and training and test set.

<p>The table displays the number of Patients, Sequences, VL measurements, and Therapies for the complete EuResist Integrated Database (EIDB) and the set of therapies that could be labeled with the definition. 469 of the sequences associated with all labeled therapies belong to historic genotypes and are not directly associated with a therapy change. Moreover, detailed information on training set and test set (comprising labeled therapies with an associated sequence) is given.</p