Discriminant functional gene groups identification with machine learning and prior knowledge

In computational biology, the analysis of high-throughput data poses several issues on the reliability, reproducibility and interpretability of the results. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list interpretability is to integrate biological information from genomic databases in the learning process. Here we propose KDVS, a machine learning based pipeline that incorporates domain biological knowledge a priori to structure the data matrix before the feature selection and classification phases. The pipeline is completed by a final step of semantic clustering and visualization. The clustering phase provides further interpretability of the results, allowing the identification of their biological meaning. To prove the efficacy of this procedure we analyzed a public dataset on prostate cancer.