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

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

2024

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 available which can be interpreted as cancer mutational view of the sample itself. Finally, we propose the use of a special Machine Learning model defined for learning similarity functions, namely the Siamese Neural Network (SNN). The proposed SNN learns to take a pair of cancer mutational views as input, and to compute a similarity score that can be used to verify whether such samples are similar or not. Preliminary experiments carried out to assess the effectiveness of the proposed system show high performance reaching f1 score 97.61%, and highlight how the similarity-based classification paradigm could be more suitable than the commonly used classification paradigm for the formulation of the cancer-type detection problem.

Paper Citation

in Harvard Style

Zaccagnino R., De Felice C., Russo M. and Zizza R. (2024). Visual Insights in Human Cancer Mutational Patterns: Similarity-Based Cancer Classification Using Siamese Networks. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-688-0, SciTePress, pages 462-470. DOI: 10.5220/0012399600003657

in Bibtex Style

@conference{bioinformatics24,
author={Rocco Zaccagnino and Clelia De Felice and Marco Russo and Rosalba Zizza},
title={Visual Insights in Human Cancer Mutational Patterns: Similarity-Based Cancer Classification Using Siamese Networks},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={462-470},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012399600003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Visual Insights in Human Cancer Mutational Patterns: Similarity-Based Cancer Classification Using Siamese Networks
SN - 978-989-758-688-0
AU - Zaccagnino R.
AU - De Felice C.
AU - Russo M.
AU - Zizza R.
PY - 2024
SP - 462
EP - 470
DO - 10.5220/0012399600003657
PB - SciTePress