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Determining gene signatures from high-throughput data

Research Area: Computational Biology and Biostatistics
Status: In progress  
Faculty: Alessandro Verri Participants: Annalisa Barla, Salvatore Masecchia, Margherita Squillario, Grzegorz Zycinski, Sofia Mosci
 
Description:

We investigate the interplay between learning techniques, prior knowledge in the molecular biology data available on a specific problem to the purpose of determining the gene signature and eventually gain a deeper  understanding of the underlying  biological process.

We are looking at:

  • Dictionary-learning-based approaches to analyze aCGH data in order to infer oncogenetic trees
  • Integration with Gene Ontology (with the Bioengineering group, DEI@UniPD): as a first step toward the combination of data driven approaches with prior knowledge, we take the Gene Ontology structure as the starting point of our gene selection framework.
  • Gliomas and Ependymomas study (with the Neuro-oncolgy unit at the Istituto Giannina Gaslini): in both cases we aim at finding genes able to discriminate between brain tumors differing for site and histology from microarray gene expression data