Visual Insights in Human Cancer Mutational Patterns: Similarity-Based Cancer Classification Using Siamese Networks

Rocco Zaccagnino, Clelia De Felice, Marco Russo, Rosalba Zizza

Abstract: In recent years, a number of innovations concerning the diagnosis and treatment of diseases through the application of genomics have opened the door to the detailed analysis of somatic mutation patterns in human cancers. Several AI-based systems have been proposed to identify correlations between mutations and type of cancer. However, the use of AI in Bioinformatics still presents two main limitations: (i) the explainability, i.e., the ability of the methods to partially explain and motivate their behavior, and (ii) the usability, i.e., about the strong limitations that are found in the actual use of such methods in real bio-medical contexts and scenarios. In this work, we propose a novel ML-based cancer-type detection system which integrates explainability and usability techniques. To this aim, we first formulate the cancer-type detection problem using the similarity-based classification paradigm. Then, given a cancer sample, we assume to have a set of somatic mutation features avai