Novel statistical tools for management of public databases facilitate community-wide replicability and control of false discovery.

Issues of publication bias, lack of replicability, and false discovery have long plagued the genetics community. Proper utilization of public and shared data resources presents an opportunity to ameliorate these problems. We present an approach to public database management that we term Quality Preserving Database (QPD).

The quality preserving database: a computational framework for encouraging collaboration, enhancing power and controlling false discovery.

The common scenario in computational biology in which a community of researchers conduct multiple statistical tests on one shared database gives rise to the multiple hypothesis testing problem. Conventional procedures for solving this problem control the probability of false discovery by sacrificing some of the power of the tests. We suggest a scheme for controlling false discovery without any power loss by adding new samples for each use of the database and charging the user with the expenses.

Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment.

Background: The extreme flexibility of the HIV type-1 (HIV-1) genome makes it challenging to build the ideal antiretroviral treatment regimen. Interpretation of HIV-1 genotypic drug resistance is evolving from rule-based systems guided by expert opinion to data-driven engines developed through machine learning methods.

Methods: The aim of the study was to investigate linear and non-linear statistical learning models for classifying short-term virological outcome of antiretroviral treatment.

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.

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.