The Rise Of Data Science In Biotechnology

Proteins are twisted and folded into specific shapes in every human cell. Protein shapes influence protein function, making them important for understanding disease mechanisms and developing new drugs. DeepMind announced in July 2021 that it had used artificial intelligence (AI) to predict the shape of nearly every protein in the human body and hundreds of thousands of proteins in other organisms. Despite varying degrees of accuracy, they could share over 350,000 newly predicted protein structures.

Omics, deep learning, and bioinformatics
The fusion of computer science and biology has traditionally been referred to as "bioinformatics," focusing on the sequencing of proteins in the early 1950s and DNA in the 1970s. Bioinformatics aims to evaluate and make sense of these enormous and intricate biological data sets, which include studies on anything from proteins to metabolites, including genomics (the study of genomes) and transcriptomics (the study of RNA transcripts).

For biotechnologists attempting to identify, stop, and cure human diseases, analyzing this data, generally known as "omics," is important. Researchers can use the data from these various fields to better understand and cure diseases. Pharmaceutical businesses do, in fact, employ biological data in a variety of ways throughout their research and development processes.