Improving classification of correct and incorrect protein-protein docking models by augmenting the training set.

Motivation: Protein-protein interactions drive many relevant biological events, such as infection, replication and recognition. To control or engineer such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions.

DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning.

Background: Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods and solutions proposed to-date, did not completely overcome these challenges.

Discriminative identification of transcriptional responses of promoters and enhancers after stimulus.

Promoters and enhancers regulate the initiation of gene expression and maintenance of expression levels in spatial and temporal manner. Recent findings stemming from the Cap Analysis of Gene Expression (CAGE) demonstrate that promoters and enhancers, based on their expression profiles after stimulus, belong to different transcription response subclasses. One of the most promising biological features that might explain the difference in transcriptional response between subclasses is the local chromatin environment.

DASPfind: new efficient method to predict drug-target interactions.

Background: Identification of novel drug-target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions.

Mining Chemical Activity Status from High-Throughput Screening Assays.

High-throughput screening (HTS) experiments provide a valuable resource that reports biological activity of numerous chemical compounds relative to their molecular targets. Building computational models that accurately predict such activity status (active vs. inactive) in specific assays is a challenging task given the large volume of data and frequently small proportion of active compounds relative to the inactive ones.

Progress and challenges in bioinformatics approaches for enhancer identification.

Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment.

Karect: accurate correction of substitution, insertion and deletion errors for next-generation sequencing data.

Motivation: Next-generation sequencing generates large amounts of data affected by errors in the form of substitutions, insertions or deletions of bases. Error correction based on the high-coverage information, typically improves de novo assembly. Most existing tools can correct substitution errors only; some support insertions and deletions, but accuracy in many cases is low.

Hi-Jack: a novel computational framework for pathway-based inference of host-pathogen interactions.

Motivation: Pathogens infect their host and hijack the host machinery to produce more progeny pathogens. Obligate intracellular pathogens, in particular, require resources of the host to replicate. Therefore, infections by these pathogens lead to alterations in the metabolism of the host, shifting in favor of pathogen protein production.

DWFS: a wrapper feature selection tool based on a parallel genetic algorithm.

Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction.

DEEP: a general computational framework for predicting enhancers.

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g.

Comparing memory-efficient genome assemblers on stand-alone and cloud infrastructures.

A fundamental problem in bioinformatics is genome assembly. Next-generation sequencing (NGS) technologies produce large volumes of fragmented genome reads, which require large amounts of memory to assemble the complete genome efficiently. With recent improvements in DNA sequencing technologies, it is expected that the memory footprint required for the assembly process will increase dramatically and will emerge as a limiting factor in processing widely available NGS-generated reads.