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The Interomics Flagship project is oriented towards the development of an integrated platform for the application of "omic" sciences to biomarker definition and theranostic, predictive and diagnostic profiles. The contribution of the research group I work with addresses the application of methods of supervised machine learning to the analysis of gene expression data. More specifically, Logic Learning Machine (LLM), is an innovative method of supervised data mining based on a set of simple intelligible rules with potential diagnostic and prognostic applications. In such a setting, LLM was proven to be an efficient method of feature selection, but its classification performance was neither assessed nor compared with other more largely applied methods of supervised analysis in large gene expression databases.

Our work is aimed at evaluating the performance of LLM method by an extensive analysis of microarray data of gene expression of human patients in comparison with standard methods of supervised analysis (namely: Decision Trees, Artificial Neural Networks, and k-Nearest Neighbours). Data were drawn from a set of publicly available databases of gene expression microarrays, stored in the GEO repository bank.

Reference paper: Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables , Stefano Parodi, Chiara Manneschi, Damiano Verda, Enrico Ferrari, Marco Muselli, Health Informatics Journal, Accepted for publication