Artificial Intelligence era begins in Genomics
Over the decades' genomics has contributed immensely in enhancing our knowledge of genes and their interactions with surrounding environment. With the exponential advancements in computational power there has been an increasing focus towards the integration of genomics in healthcare with availability of tools for rapid interpretation of genetic information which could be used in development of drugs, disease prediction, diagnosis, therapy selection and progression with reduced cost. Thus, much attention has been paid towards advancing genomics approaches like sequencing, genome editing and other technologies to increase their utilization in drug discovery & development, disease research, diagnostics and other applications. One of the evolving approaches is the integration of AI and machine learning technologies to revolutionize genomics by analyzing genomic data sets (e.g. associations between variants and molecular or physiological measures of health/disease), which could then provide new parameters for healthcare personalization, identify new disease biomarkers, and refine the understanding of the disease.
Machine learning can help improve accuracy in the sequencing process. For instance, Variant calling is one of the bioinformatics analyses of sequencing data for the detection of genetic variants that may underline diseases. Several deep learning tools are being developed to further improve the accuracy of the variant calls. The high-throughput sequencing and functional genomics techniques (e.g. methods to analyze protein interaction with DNA) will generate larger and more detailed datasets that can aid in the discovery and prediction of genetic diseases. Deep learning approaches are being leveraged to analyze these datasets given their higher capacity for modelling complexity and discovering patterns buried within the details of these generated data. The analysis and interpretation of genomic sequence data sets using AI approach will play an important role in target identification in drug discovery, designing of personalized medicine and diagnosis of diseases. For instance, the use of AI in the genetic screening of fetuses is increasingly becoming a standard practice. This non-invasive genetic screening can detect diseases like Down syndrome and other genetic syndromes during pregnancy. Genome editing has revolutionized research on the human genome, which has enabled investigators to better understand the contribution of a single-gene product to disease in an organism. A novel approaches like the use of machine learning algorithms to identify the precise location for DNA alteration helping in reducing potential mistakes during the entire process. Researchers would be able to computationally predict the right guide RNAs that would reproduce exact mutations, to develop better research models for studying genetic diseases. This will further enhance the application of genome editing.
Companies are adopting AI platforms for data analysis & interpretation in disease research, clinical trials and to provide genetic services in order to achieve the best outcomes. For instance, in July 2019, Dante Labs launched new artificial intelligence (AI) tool to create personalized reports from whole-genome sequencing data that leverages a person's medical information to identify the most relevant insights for diseases such as cancers, epilepsy, diabetes, Alzheimer's, Parkinson's disease and other rare diseases. In September 2020, Congenica launched a new module for its AI-based genomics analysis platform for COVID -19 disease research. This platform analyzes the relationship between an individual's genomic variation and COVID-19 susceptibility, severity, and clinical outcomes, which further support the development of novel therapeutics. Big pharma companies such as Astra Zeneca are planning to analyze up to 2 million genomes and study huge amounts of patient data points from their drug clinical trials using AI by 2026. Also, companies are receiving funding to establish AI based genomic platform for data analysis. For instance, in April 2019, Congenica received $17.1 million series B funding for the development of the next generation Sapientia platform to bring AI-mediated decision augmentation and automation to rare disease and cancer therapy management. Similarly, in February 2019, Emedgene, received $6 million funding to scale genomics-based care with AI. The platform interprets genetic tests automatically, helping improve the workflow of geneticists and providing more accurate results to scale genomics-based care with artificial intelligence (AI). Thus, overall the adoption of AI into genomics will enhance the utilization of genomics in different applications such as diagnosis, drug discovery & development and disease research in the future.
The genomics technologies will helps in rapid implementation of genomics in healthcare mainly in the diagnosis both infectious and non - infectious diseases, in basic research - to study the overall genetic mechanism behind the disease and in drug discovery and development - to identify the potential therapeutic targets, validation of target & to develop companion diagnostics. According to IQ4I analysis, the genomics global market is estimated to be $20,064.9 million in 2020 and is expected to reach $31,835.9 million by 2027 growing at a CAGR of 6.8% from 2020 to 2027.
The key players in Genomics Global Market include F. Hoffmann-La Roche Ltd. (Switzerland), Illumina Inc. (U.S.), Thermo Fisher Scientific, Inc. (U.S.), Qiagen N.V. (Netherlands), Danaher Corporation. (U.S.), Exact Sciences Corporation (U.S.), Myriad Genetics (U.S.) Bio-Rad Laboratories, Inc. (U.S.), Hologic, Inc (U.S.), Merck KGaA (Germany), Abbott Laboratories (U.S.), BGI Genomics (China) and others.