Machine learning in precision medicine: Advancement, challenges and future directions

---

Adebayo Adewale

OneHealth Tech, Nigeria

: J Comput Eng Inf Technol

Abstract

---

Precision medicine is a cutting-edge approach to disease prevention and treatment that takes into consideration the individual variability in genes, environment, and lifestyle. Machine learning techniques offer great promise in facilitating precision medicine by identifying patterns in large-scale biomedical datasets. In the field of oncology, machine learning models that utilize clinical, genomic, and imaging data have demonstrated enhanced predictive abilities for cancer prognosis and personalized treatment compared to traditional methods. Moreover, neural networks applied to gene expression data have led to more accurate classification of breast cancer subtypes. Machine learning tools have also shown potential in uncovering novel insights into disease mechanisms. For instance, network-based algorithms have identified potential drug targets and inferred relationships between drug response and tumor mutations. In the realm of pharmacogenomics, machine learning studies have revealed genetic variants associated with drug response phenotypes. Additionally, machine learning techniques have proven useful in precision psychiatry by discerning patterns in neuroimaging data that differentiate mental health disorders. Despite these promising applications, integrating diverse biomedical datasets, avoiding model overfitting, and ensuring model interpretability for clinical decisionmaking remain as challenges. Overall, machine learning is a rapidly advancing technique with the potential to revolutionize diagnosis, prognosis prediction, and personalized treatment in various areas of precision medicine. However, further research and interdisciplinary collaboration are necessary to develop robust and interpretable models suitable for clinical application. Recent Publications: 1. MacEachern, S. J., & Forkert, N. D. (2021). Machine learning for precision medicine. Genome, 64(4), 416–425. https:// doi.org/10.1139/gen-2020-0131 2. Cammarota, G., Ianiro, G., Ahern, A., Carbone, C., Temko, A., Claesson, M. J., Gasbarrini, A., & Tortora, G. (2020). Gut microbiome, big data and machine learning to promote precision medicine for cancer. Nature Reviews. Gastroenterology & Hepatology, 17(10), 635–648. https://doi.org/10.1038/s41575-020-0327-3 3. Plant, D., & Barton, A. (2021). Machine learning in precision medicine: lessons to learn. Nature Reviews Rheumatology, 17(1), 5–6. https://doi.org/10.1038/s41584-020-00538-2 4. Sahu, M., Gupta, R., Ambasta, R. K., & Kumar, P. (2022, January 1). Chapter Three - Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis(D. B. Teplow, Ed.). ScienceDirect; Academic Press. https://www.sciencedirect.com/science/article/abs/pii/S1877117322000436 5. Sahu, M., Gupta, R., Ambasta, R. K., & Kumar, P. (2022, January 1). Chapter Three - Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data. https://www.sciencedirect.com/science/article/abs/pii/ S1877117322000436.

Biography

---

Adebayo Adewale is an information technology student at the Federal University of Technology, Akure, Nigeria pursuing bioinformatics research with the OneHealth Tech Community in Ibadan. His passion lies at the intersection of machine learning and precision medicine. Adewale is particularly interested in developing AI and machine learning solutions to advance healthcare. As a young researcher, he aims to leverage his IT background and involvement in the tech community to push innovations in the emerging field of biomedical informatics. Adewale is focused on addressing key challenges like integrating heterogeneous data, avoiding model overfitting, and promoting model interpretability. His goal is to pave the way for practical clinical applications.