Machine learning applications in precision medicine: Overcoming challenges and unlocking potential
Henning Nilius, Sofia Tsouka, Michael Nagler, Mojgan Masoodi
Abstract
Precision medicine, utilizing genomic and phenotypic data, aims to tailor treatments for individual patients. However, successful implementation into clinical practice is challenging. Machine learning (ML) algorithms have demonstrated incredible capabilities in handling probabilities, managing diverse datasets, and are increasingly applied in precision medicine research. The key ML applications include classification for diagnosis, patient stratification, prognosis, and treatment monitoring. ML offers solutions for automated structural elucidation, in silico library construction, and efficient processing of mass spectrometry raw data. Integration of ML with genome-scale metabolic models (GEMs) provides mechanistic insights into genotype-phenotype relationships. In this manuscript, we examine the impact of ML in various facets of precision medicine, from diagnostics and patient phenotyping to personalized treatment strategies. Finally, we propose a methodological framework for implementing ML applications in clinical practice, emphasizing a step-by-step approach, starting with the identification of clinical needs and research questions, followed by development, validation, and implementation.