Magnifying Future Genomics with Machine Learning

Pharma Tech Outlook: Pharma Tech Magazine

Magnifying Future Genomics with Machine Learning

By Pharma Tech Outlook | Wednesday, September 09, 2020

By integrating machine learning into genomics, the researchers and scientists can magnify their efficiency and functionality.  

FREMONT, CA: "Curiosity is the wick in the candle of learning," once quoted by William Arthur Ward, projects the present drive for a fathomable future. As technological innovations open the gates of scientific possibilities, the passion for cracking life's mystery grows stronger and stronger. From creation to annihilation, the answer to all equations lies in the genes.     

Genomics is a subset of molecular biology that centers on learning all elements of a genome and the entire gene set within a specific organism. As evolution resumes, the functionalities of genetics become very complicated. However, digital disruption and technological advancement already have a solution to elevating difficulties.

The capacity to sequence DNA enables scientists to tap into the genetic blueprint that guides all of a living organism's operations. Furthermore, the central dogma is illustrated as the mechanism of converting DNA to RNA to Protein to provide context.

To understand the vastness of the subject, one needs to dive into the plethora of unique anomalies fully. DNA consists of base pairs based on four fundamental units, which gives rise to chromosomes, 46 in case of humans. Chromosomes are also structured into DNA sections called genes that create or encode proteins. The amount of genes possessed by an organism is dubbed as the genome. Around 20,000 genes and 3 billion base pairs are obtainable to human beings. Ironically, only about 2 percent of the human genome encodes proteins, and this is the focus region of research and proteomics companies.

Precision Medicine is a patient care strategy that includes genetics, attitudes, and climate to apply a patient or population-specific therapy procedure as opposed to a one-size-fits-all approach. In the case of blood donation, a recipient who happens to share the same type of blood would be paired instead of a randomly selected donor to minimize the risk of abnormalities.

There are currently two significant obstacles to more accuracy medication application, high costs, and technology constraints. Many scientists are applying machine learning methods to address the vast quantity of patient information that needs to be gathered and evaluated for reducing expenses. Luckily, the price of sequencing a genome continues to decline year after year for scientists and genomics businesses.

Presently, Machine learning is playing an essential part in the development of the genomics study. Researchers are now using deep learning to detect trends in genetic data sets of large quantity. Furthermore, these patterns are being converted into computer models which can predict the likelihood of an individual getting certain illnesses or guide the development of prospective therapies.

Direct-to-Consumer genomics includes businesses that deliver facilities to individual customers for genomic sequencing. In recent times, businesses are using machine learning to gain a deeper understanding of genetic data, such as how the genes of an individual can affect the basic functionalities.

Machine Learning Applications in Genomics

Whole-genome sequencing (WGS) has evolved from a medical diagnostic to a region of concern. Relative to the conventional Sanger sequencing technology that needed finalization over a decade when the human genome was first sequenced, the next evolutionary computation has emerged as an efficient technique that embraces modern DNA microarray techniques, enabling scientists to sequence an entire human genome in a single day.

Check Out: Top Genomics Companies

In order to assist scientists in understanding genetic variation, enterprises are using data analytics. In particular, algorithms are intended based on patterns recognized in big genetic data sets that are then converted into computer models to assist customers in perceiving how critical cellular procedures are affected by genetic variation.

Gene Editing

Gene editing is described as a technique of creating cellular or organism-level particular changes to DNA. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a technology for gene editing that offers a quicker and far less pricey way to export genes. Furthermore, scientists had to first pick a suitable target pattern to use CRISPR, which can be a daunting method that involves numerous decisions and unpredictable results. However, machine learning provides the ability to shorten the time, price, and energy required to identify a suitable target sequence considerably. Moreover, the increasing amount of dataset improves the reliability and accuracy of the CRISPR activity algorithm.

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