Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine?

Front Genet. 2024 Jan 12:14:1304661. doi: 10.3389/fgene.2023.1304661. eCollection 2023.

Abstract

Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.

Keywords: NGS -next generation sequencing; bioinformatics; chronic lymphocytic leukemia (CLL); machine Learning; multi-omics analysis; omics; precision medicine.

Publication types

  • Review

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research has received funding from the European Union’s Horizon 2020 research and innovation programme through the ERC Synergy project BCLL@atlas under grant agreement No 810287 (IGG). MT was granted from the Spanish Instituto de Salud Carlos III with “Sello de Excelencia ISCIII-HEALTH” Plan de Recuperación, Transformación y Resiliencia, con código de expediente IHMC22/00027y Financiado por la Unión Europea–NextGenerationEU. Institutional support was from the Spanish Instituto de Salud Carlos III, Fondo de Investigaciones Sanitarias and cofunded with ERDF funds (PI19/01772).