Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival.

In order to find clinically useful prognostic markers for glioma patients' survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. The input features were ranked according to their importance in predicting patients' longer (400+ days) or shorter (≤400 days) survival without prior classification of the patients. Interestingly, out of the 65 most important features found, 63 are methylation sites, and only two mRNAs.

Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors.

Motivation: Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility.

A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome.

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations.

Monte Carlo feature selection for supervised classification.

Motivation: Pre-selection of informative features for supervised classification is a crucial, albeit delicate, task. It is desirable that feature selection provides the features that contribute most to the classification task per se and which should therefore be used by any classifier later used to produce classification rules. In this article, a conceptually simple but computer-intensive approach to this task is proposed.