Quantum machine learning for untangling the real-world problem of cancers classification based on gene expressions

Zarei Ghobadi, M.; Afsaneh, E.

doi:10.1101/2023.08.09.552597

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bioRxiv · 10.1101/2023.08.09.552597

Quantum machine learning for untangling the real-world problem of cancers classification based on gene expressions

Zarei Ghobadi, M.·Afsaneh, E.

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Abstract

Quantum machine learning algorithms using the power of quantum computing provide fast- developing approaches for solving complicated problems and speeding-up calculations for big data. As such, they could effectively operate better than the classical algorithms. Herein, we demonstrate for the first time the classification of eleven cancers based on the gene expression values with 4495 samples using quantum machine learning. In addition, we compare the obtained quantum classification results with the classical outcomes. By implementing a dimensional reduction method, we introduce significant biomarkers for each cancer. In this research, we express that some of the identified gene biomarkers are consistent with DNA promotor methylation, and some other ones can be applied for the survival determination of patients.

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Zarei Ghobadi, M., Afsaneh, E.. 2023-08-14. Quantum machine learning for untangling the real-world problem of cancers classification based on gene expressions. https://doi.org/10.1101/2023.08.09.552597

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