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

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New method for true-triaxial rock testing

Two new and related true-triaxial apparatus are described that make use of conventional triaxial pressure vessels in combination with specially configured, high-pressure hydraulic jacks inside these vessels. The development combines advantages not found in existing facilities, including a compact design, pore-pressure and flow-through capabilities, the ability to attain high principal stresses and principal stress differences, direct access to parts of the sample, and provisions to go to relatively large deformations without developing serious stress field inhomogeneities

Monte Carlo feature selection and interdependency discovery in supervised classification

Applications of machine learning techniques in Life Sciences are the main applications forcing a paradigm shift in the way these techniques are used. Rather than obtaining the best possible supervised classifier, the Life Scientist needs to know which features contribute best to classifying distinct classes and what are the interdependencies between the features. To this end we significantly extend our earlier work [Dramiński et al. (2008)] that introduced an effective and reliable method for ranking features according to their importance for classification. We begin with adding a method for finding a cut-off between informative and non-informative fea- tures and then continue with a development of a methodology and an implementa- tion of a procedure for determining interdependencies between informative features. The reliability of our approach rests on multiple construction of tree classifiers. Essentially, each classifier is trained on a randomly chosen subset of the original data using only a fraction of all of the observed features. This approach is conceptually simple yet computer-intensive. The methodology is validated on a large and difficult task of modelling HIV-1 reverse transcriptase resistance to drugs which is a good example of the aforementioned paradigm shift. We construct a classifier but of the main interest is the identification of mutation points (i.e. features) and their combinations that model drug resistance.feature selection, interdependency discovery, MCFS-ID, biological sequence analysi

Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach

The Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding site