Machine Learning and the self-terminating gene - AI Med

Machines learning promises us a future where hefty and complex data can be analyzed in no time and patterns can be logically drawn from an ocean of randomness. Apart from electronic medical records, the human gene and genome has also rendered a sizeable playing field. Machine learning method is either supervised: training involving labeled DNA sequence which marks the start and end location of a gene; unsupervised: training which takes place without training data, or a hybrid of both. The generalizable predictive nature of machine learning had enabled us to predict the impact of drug on a person with DNA mutation (i.e., pharmacogenomics), functional consequences of DNA mutations (i.e., pathogenicity prediction), how a locus mutation impact the expression level of a gene (eQTL mapping) and many more. The peak of this recent hype lies in Google's DeepVariant release last November, when high-throughput sequencing (HTS) is now guaranteed with a greater accuracy via deep learning.