Machine learning: a tool to shape the future of medicine

The constant evolution of biomedicine, biophysics and biochemistry has enabled scientists to investigate and study each cell identity via analyzing the transciptome and its kinetics, the chromatin accessibility patterns but also via investigating the structure of proteins and RNA. Taking all this under consideration, scientists have developed algorithms and machine learning (ML) schemes that take advantage of the current state-of-the-art approaches to predict the cell states, discover the exact 3D structure of proteins and RNA and most importantly evaluate personalized medicine approaches via predicting drugs and specific immunotherapy treatments. Moreover, the recent advances in ML and chemo-informatics have also paved the way for drug repurposing models, thus evaluating and establishing in silico novel treatments. The aim of this chapter is to provide and analyze the mathematics behind such ML techniques and review the current applications being developed that walk side by the side with the continuous progress of biosciences.

VG New

Prof. Vassilis G. Gorgoulis

Laboratory of Histology-Embryology
Molecular Carcinogenesis Group
Medical School
National and Kapodistrian University of Athens



Chair of Clinical Molecular Pathology,

Ninewells Hospital and School of Medicine


University of Dundee, Dundee, UK


Biomedical Research Foundation of the Academy of Athens


Faculty Institute for Cancer Sciences, University of Manchester,
Manchester Academic Health Science Centre, Manchester, UK

Manchester Centre for Cellular Metabolism,
University of Manchester, Manchester Academic Health Science Centre, Manchester


EMBO member


European Academy
of Cancer Sciences member


Academia Europaea member, 180 Varick Street, 6th Floor, New York, NY 10014, USA 






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