In the era of data-driven science, conducting computational experiments that involve analysing large datasets using heterogeneous computational clusters, is part of the everyday routine for many scientists. Moreover, to ensure the credibility of their results, it is very important for these analyses to be easily reproducible by other researchers. Although various technologies, that could facilitate the work of scientists in this direction, have been introduced in the recent years, there is still a lack of open-source platforms that combine them to this end. In this work, we describe and demonstrate SCHeMa, an open-source platform that facilitates the execution and reproducibility of computational analysis on heterogeneous clusters, leveraging containerization, experiment packaging, workflow management, and machine learning technologies.

MicroRNAs (miRNAs) are small RNA molecules that inhibit the expression of particular genes, a function that makes them useful towards the treatment of many diseases. Computational methods that predict which genes are targeted by particular miRNA molecules are known as target prediction methods. In this paper, we present a MapReduce-based system, termed MR-microT, for one of the most popular and accurate, but computational intensive, prediction methods. MR-microT offers the highly requested by life scientists feature of predicting the targets of ad-hoc miRNA molecules in near-real time through an intuitive Web interface.

We consider the problem of allocating bandwidth between two endpoints of a backbone network so that no parts of the network are unnecessarily loaded. We formulate the problem as lexicographic optimization and develop algorithms for its solution. The solution consists of: (1) identifying a cut in the network where the optimal load can be determined on all the links of the cut and (2) considering the same problem for each of the subnetworks to which the cut is dividing the original network.

We consider the problem of allocating bandwidth between two endpoints of a backbone network so that no parts of the network are unnecessarily loaded. We formulate the problem as lexicographic optimization, and develop algorithms for its solution. The solution consists of (a) identifying a cut in the network where the optimal load can be determined on all the links of the cut, and (b) considering the same problem in each of the subnetworks to which the cut is dividing the original network.