Author:
Ghoabdi Mohadeseh Zarei,Afsaneh Elaheh
Abstract
AbstractQuantum 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.
Publisher
Cold Spring Harbor Laboratory
Reference62 articles.
1. Quantum machine learning: From physics to software engineering;Advances in Physics: X,2023
2. Quantum machine learning: A review and case studies;Entropy,2023
3. Exploration of mRNAs and miRNA classifiers for various ATLL cancer subtypes using machine learning;BMC cancer,2022
4. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review;Diabetology Metabolic Syndrome,2022
5. Integration of gene co-expression analysis and multi- class SVM specifies the functional players involved in determining the fate of HTLV-1 infection toward the development of cancer (ATLL) or neurological disorder (HAM/TSP);Plos one,2022