Institute of Theoretical and Applied Informatics, Polish Academy of Sciences

Time series analysis is an important field of data engineering. In this seminar we present the statistical method dedicated to time series analysis, provided the series mimic some form of the complex physical system. The method uses recurrence plot and Hurst exponent approaches. So far, the method was used for data related to molecular dynamics and climate (temperature) observations. Hence, our method tackles very important climate changes issue.

The prediction of interactions between drugs (medicines) and pharmacological targets (proteins) is one of the most prominent applications of machine learning it the pharmaceutical industry. From the theoretical point of view, the task is to predict unknown links in a bipartite graph. Compared with standard link prediction, in case of drug-target interaction prediction, additional information is available, such as the similarity between pairs of drugs and the similarity between pairs of targets.