The high performance of neural networks in classification and prediction tasks, makes them being applied to practically all areas where large amounts of data are available. Given their complicated mathematical structure, they have almost always been considered black boxes, without providing information about the mechanisms that contribute to the output. Yet, for some applications, two notable examples being bioinformatics and finance, is of paramount importance to know the criteria that lead to a given output.
The goal of this project is twofold. First it will advance significantly the state of the art by proposing, analyzing, and evaluating techniques for understanding how input features in neural-network-based classification functions interact with each other and what is the effect of these interactions to the network output.
Second it will apply these findings to the fields of bioinformatics and of finance. Regarding the former, the application scenario is the discovery of epistatic interactions, which regards the detection of the interaction between genomes in the human genome. The second application is predictions of company default: what factors can lead to a company default within a 12-month period?
Both of these applications will be based on the analysis of precious and high quantity and quality datasets.
The team is composed from experts in Data Science and Big Data analysis, as well as experts in the two application areas, who will collaborate to bring advancements to the areas of computer science, as well as specific contributions to the respective fields.
