Title:The Use of Machine Learning in MicroRNA Diagnostics: Current
Perspectives
Volume: 11
Issue: 3
Author(s): Chrysanthos D. Christou*, Angelos C. Mitsas, Ioannis Vlachavas and Georgios Tsoulfas
Affiliation:
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of
Thessaloniki, Thessaloniki, Greece
Keywords:
Computer algorithms, liver cancer, gestational diabetes mellitus, alcohol dependence, artificial intelligence, Alzheimer’s disease.
Abstract: MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the
translation and degradation of mRNA and have been associated with many diseases. Artificial Intelligence
(AI) is an evolving cluster of interrelated fields, with machine learning (ML) standing out as
one of the most prominent AI fields, with a plethora of applications in almost every aspect of human
life. ML could be defined as computer algorithms that learn from past data to predict future data. This
review comprehensively reviews the current applications of microRNA-based ML models in
healthcare. The majority of the identified studies investigated the role of microRNA-based ML models
in the management of cancer and specifically gastric cancer (maximum diagnostic accuracy (Accmax):
94%), pancreatic cancer (Accmax: 93%), colorectal cancer (Accmax: 100%), breast cancer
(Accmax: 97%), ovarian cancer, neck squamous cell carcinoma, liver cancer, lung cancer (Accmax:
100%), and melanoma. Except for cancer, microRNA-based ML models have been applied for a
plethora of other diseases, including ulcerative colitis (Accmax: 92.8%), endometriosis, gestational
diabetes mellitus (Accmax: 86%), hearing loss, ischemic stroke, coronary heart disease (Accmax:
96%), tuberculosis, pulmonary arterial hypertension (Accmax: 83%), dementia (Accmax: 82.9%),
major cardiovascular events in end-stage renal disease patients, and alcohol dependence (Accmax:
79.1%). Our findings suggest that the development of microRNA-based ML models could be used to
enhance the diagnostic accuracy of a plethora of diseases while at the same time substituting or minimizing
the use of more invasive diagnostic means (such as endoscopy). Even not as fast as anticipated,
AI will eventually infiltrate the entire healthcare industry. AI is the key to a clinical practice
where medicine's inherent complexity is embraced. Therefore, AI will become a reality that physicians
should conform with to avoid becoming obsolete.
