Machine learning in postgenomic biology and personalized medicine

Abstract

In recent years, machine learning (ML) has been revolutionizing biology, biomedical sciences, and gene-based agricultural technology capabilities. Massive data generated in biological sciences by rapid and deep gene sequencing and protein or other molecular structure determination, on the one hand, require data analysis capabilities using ML that are distinctly different from classical statistical methods; on the other, these large datasets are enabling the adoption of novel data-intensive ML algorithms for the solution of biological problems that until recently had relied on mechanistic model-based approaches that are computationally expensive. This review provides a bird's eye view of the applications of ML in postgenomic biology. Attempt is also made to indicate as far as possible the areas of research that are poised to make further impacts in these areas, including the importance of explainable artificial intelligence in human health. Further contributions of ML are expected to transform medicine, public health, agricultural technology, as well as to provide invaluable gene-based guidance for the management of complex environments in this age of global warming.

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