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Background In the Next Generation Sequencing (NGS) era a large amount of biological data is being sequenced, analyzed, and stored in many public databases, whose interoperability is often required to allow an enhanced accessibility. The combination of heterogeneous NGS genomic data is an open challenge: the analysis of data from different experiments is a fundamental practice for the study of diseases...
Background The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be addressed with supervised machine learning techniques able to extract human interpretable models composed of genes, and their relation to the investigated...
Due to the great advances of Next Generation Sequencing (NGS) techniques, bioinformaticians are faced with large amounts of genomic and clinical data, which are growing exponentially. A striking example is The Cancer Genome Atlas (TCGA), whose aim is to provide a comprehensive archive of biomedical data about tumors. Indeed, TCGA contains more than 15 TB of genomic and clinical data, whose analysis...
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