Technologies such as machine learning (ML) is significantly assisting in the evolution and impacting multiple aspects in the field of genomics.
FREMONT, CA: Genomics involves the study of all aspects of a genome or the entire set of genes within a particular organism. Technologies such as machine learning (ML) is significantly assisting in the evolution of the field of genomics. Genomics is also closely linked to Precision medicine. The field of precision medicine is an approach to patient care that permeates genetics, environment, and behaviors with a goal of deploying a patient or population-specific treatment intervention. There are two major barriers to greater incorporation of precision medicine- technology limitations and high costs. ML can be crucial here due to its ability to collect and analyze a vast amount of patient data. It will also help in cutting down on costs in the future.
Check This Out: Top Genomics Companies
At present, applications of ML in genomics come under the following two categories:
ML is being used by the researchers to identify patterns within the huge volume of genetic data sets. The patterns are then translated by computer models that can help predict an individual’s chances of contracting certain diseases or enable him to understand the design of potential therapies.
It includes companies that provide genomic sequencing services to each customer. Firms are leveraging ML to gain an in-depth understanding of genetic information such as the effect of an individual’s genes on his weight.
Applications of AI and ML in Genomics
The use cases of ML in the field of genomics are impacting the genetic researches. Here are some of the insights into that:
Whole Genome Sequencing (WGS) has expanded as an area of interest in medical research. Next-Generation Sequencing is also emerging as a buzzword that includes modern DNA sequencing techniques that allow researchers to sequence an entire human genome in a day which is in contrast with the classic Sanger sequencing technology that required over a decade for accomplishment when the human genome was first sequenced.
Gene editing refers to a method of making certain alterations to DNA at the organism or cellular level. CRISPR offers a faster and less expensive way of conducting gene editing. However, researchers first need to select an appropriate target sequence. It can be an uphill task involving several choices and unpredictable outcomes. ML provides the capability to decrease time significantly, cost, and effort, which is needed to spot an appropriate target sequence.
Patient data often contain gaps which are available to various members of a healthcare team which is serving a patient. The issue has led to an increased interest in using ML to enhance the efficiency of the clinical workflow process.