Data Science

Contrary to the reductionist approach aimed at understanding individual components, the new data revolution allows the understanding of complicated interactions and  pathways through the use of statistical Data Mining/Machine-Learning techniques. This is an active area of research in computer science with the increasing availability of big data collections of all sorts prompting interest in the development of novel tools for data mining. It is expected that continuous improvement of software infrastructure will make ML applicable to a growing range of biological problems.We envision, plan and manage large and completely independent projects that merged knowledge from chemistry, bioinformatics, biophysics, statistics and data-mining to build innovative and completely new methods and computational solutions. Striking examples are the application of ML to (a) the detection of Hot-Spots; (b) to the understanding of protein-protein interfacial (PPI) characteristics; (c) to the role of co-evolution in PPIs; (d) to the construction of web-servers such as SBHD and SpotON as well as (e) to small-ligand binding IC50 prediction. We are now involved in the development of new methodologies to be applied to membrane proteins and drug design.