Application of machine learning for high-throughput tumor marker screening.

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.
Competing Interests: Declaration of competing interest The authors declare no conflict of interest. Xingxing Fu drafted the manuscript. Qi Zuo and Wanting Ma searched part of the literature. Xingxing Fu, Yinan Zhao, Yanfei Qi and Shubiao Zhang conceived and designed the work. Xingxing Fu, Yinan Zhao, Yanfei Qi and Shubiao Zhang edited and revised the manuscript. All the authors have read and approved the final manuscript.
(Copyright © 2024. Published by Elsevier Inc.)