Knowledge bases and stochastic algorithms for mining biological data: applications on A-to-I RNA editing and RNAi

Abstract

Until the second half of twenty century, the connection between Biology and Computer Science was not so strict and the data were usually collected on perishable materials such as paper and then stored up in filing cabinets.

This situation changed thanks to the Bioinformatics, a relatively novel field that aims to deal with biological problems by making use of computational approaches. This interdisciplinary science has two particular fields of action: on the one hand, the construction of biological databases in order to store in a rational way the huge amount of data, and, on the other hand, the development and application of algorithms also approximate for extracting predicting patterns from such kind of data.

This thesis will present novel results on both of the above aspects. It will introduce three new database called miRandola, miReditar and VIRGO, respectively. All of them have been developed as open sources and equipped with user-friendly web interfaces.

Then, some results concerning the application of stochastic approaches on microRNA targeting and RNA A-to-I interference will be introduced.