Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification

• URL: http://arxiv.org/abs/2410.02085v1
• Date: Wed, 2 Oct 2024 23:16:31 GMT
• Title: Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification
• Authors: Mandeep Kaur Saggi, Amandeep Singh Bhatia, Mensah Isaiah, Humaira Gowher, Sabre Kais,
• Abstract summary: The fusion of quantum computing and machine learning holds promise for unraveling complex patterns within multi-omics datasets. We developed a method for finding the best differentiating features between LUAD and LUSC datasets, which has the potential for biomarker discovery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Machine Learning (QML) is a red-hot field that brings novel discoveries and exciting opportunities to resolve, speed up, or refine the analysis of a wide range of computational problems. In the realm of biomedical research and personalized medicine, the significance of multi-omics integration lies in its ability to provide a thorough and holistic comprehension of complex biological systems. This technology links fundamental research to clinical practice. The insights gained from integrated omics data can be translated into clinical tools for diagnosis, prognosis, and treatment planning. The fusion of quantum computing and machine learning holds promise for unraveling complex patterns within multi-omics datasets, providing unprecedented insights into the molecular landscape of lung cancer. Due to the heterogeneity, complexity, and high dimensionality of multi-omic cancer data, characterized by the vast number of features (such as gene expression, micro-RNA, and DNA methylation) relative to the limited number of lung cancer patient samples, our prime motivation for this paper is the integration of multi-omic data, unique feature selection, and diagnostic classification of lung subtypes: lung squamous cell carcinoma (LUSC-I) and lung adenocarcinoma (LUAD-II) using quantum machine learning. We developed a method for finding the best differentiating features between LUAD and LUSC datasets, which has the potential for biomarker discovery.

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