The Key of Life – Gene Analysis

3rd Jun 2019

One area that machine learning is significantly evolving is genomics—the study of the complete set of genes within an organism. While much attention has been paid to the implications for human health, genetic sequencing and analysis could also be ground-breaking for agriculture and animal husbandry. When researchers can sequence and analyze DNA, something that artificial intelligence systems make faster, cheaper and more accurate, they gain perspective on the particular genetic blueprint that orchestrates all activities of that organism. With this insight, they can make decisions about care, what an organism might be susceptible to in the future, what mutations might cause different diseases and how to prepare for the future. Artificial intelligence and machine learning help make gene editing initiatives more accurate, cheaper and easier.

Multiple aspects of human life are determined by an individual’s genetics, including predispositions to illnesses such as cystic fibrosis, Huntington’s disease, sickle cell anemia, and others. Better understandings of genetic makeup can help scientists comprehend, predict, and even change the function of genes. The introduction of machine learning into this world will add a possibility of accuracy and wide-scale efficiency to both gene sequencing and gene editing.

The future for AI and gene technology is expected to include pharmacogenomics, genetic screening tools for newborns, enhancements to agriculture and more. While we can’t predict the future, one thing is for sure: AI and machine learning will accelerate our understanding of our own genetic makeup and those of other living organisms.

Our Mission in HITS is to develop an AI/Machine learning tool for faster and more accurate gene analysis, interpreting gene data live while gene is being sequenced. Working on a set of machine learning algorithms that uncover clinically meaningful connection models within the AI knowledge. Using these algorithms, we can successfully pinpoint the potentially causative mutations for both known and unknown genes.