Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.</p> <p>Results</p> <p>We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.</p> <p>Conclusions</p> <p>Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.</p

Ensemble learning algorithms for classification of mtDNA into haplogroups

Classification of mitochondrial DNA (mtDNA) into their respective haplogroups allows the addressing of various anthropologic and forensic issues. Unique to mtDNA is its abundance and non-recombining uni-parental mode of inheritance; consequently, mutations are the only changes observed in the genetic material. These individual mutations are classified into their cladistic haplogroups allowing the tracing of different genetic branch points in human (and other organisms) evolution. Due to the large number of samples, it becomes necessary to automate the classification process. Using 5-fold cross-validation, we investigated two classification techniques on the consented database of 21 141 samples published by the Genographic project. The support vector machines (SVM) algorithm achieved a macro-accuracy of 88.06% and micro-accuracy of 96.59%, while the random forest (RF) algorithm achieved a macro-accuracy of 87.35% and micro-accuracy of 96.19%. In addition to being faster and more memory-economic in making predictions, SVM and RF are better than or comparable to the nearest-neighbor method employed by the Genographic project in terms of prediction accuracy