This is the abstract of a talk prepared for the Oeiras Mathematical and Computational Biology Workshop. June 20, 2003, Instituto Gulbenkian de Ciência
Abstract: The research of the Knowledge Discovery and Bioinformatics group (KDBIO) of INESC-ID focuses on the application of inductive inference and data mining techniques to the extraction of knowledge from large sets of unstructured data.
We intend to use our expertise in algorithmical and statistical inference techniques to analyze the massive amounts of biological data generated by biological experiments. In particular, we are developing new techniques to infer probabilistic networks with minimal description complexity that model the complex interactions inherent to regulatory networks. We are also pursuing an effort on the development of algorithms for the identification of commonly occurring strings in genomic sequences that justify the coordinated gene transcriptional regulation revealed by genome-wide monitoring.
Currently, an effort is being undertaken in our group, in cooperation with the Biological Sciences Group of IST, to build a publicly available database of Yeast transcriptional activators and the respective promoter consensus motifs in target genes. This database will integrate the large amounts of currently available information about gene regulation in Saccharomyces cerevisiae.