The technical advances achieved in the last years by genomics, transcriptomics, proteomics and metabolomics, have significantly increased the amount of data that can be measured from different aspects of an organism. These large volumes of data are generated very easily, but then they must be transformed into information. Within data there are intricate hidden internal relations, which today represent a big challenge for discovery and analysis.
The current challenge is the fusion of such different types of biological data to be able to discover these hidden relationships, in order to infer new knowledge about the biological processes that involve them. New computational methods are required to automatically perform typical data mining tasks, such as standardization, selection and integration of heterogeneous sources, class discovery and semi-supervised classification, clustering, and new knowledge inference.
We are make algorithms for biological data integration/fusion/analysis. We develop novel data mining algorithms and computing tools for biological data fusion, integration and new knowledge discovery. Current research lines:
- Gene function prediction and automatic inference of GO annotations to genes
- Gene regulatory networks reconstruction from gene data
- De-novo metabolic pathways search
- Pre-miRNA prediction algorithms from genome-wide data
- Data fusion and inference from heterogeneous biological sources
PICT 2014 ANPCyT
PIP 2013 CONICET
CAID 2016 UNL
PICT 2008 ANPCyT
PIP 2010 CONICET
PID 2012 UTNFRSF
PE BIO 2011 INTA