The Future of AI in Genomics
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The Future of AI in Genomics

Pharma Tech Outlook | Tuesday, August 20, 2019

The inception of AI and ML in genomics will facilitate a greater understanding of the genome, thus empowering more accurate predictions.

FREMONT, CA: The genomics landscape has witnessed significant changes in the last few years. The rapid evolution of the clustered regularly interspaced short palindromic repeats (CRISPR) technology has shown great potential in applications ranging from gene editing to drug development. Amid the ethical backlash, an increasing number of organizations are investing in the technology.

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The capabilities of the CRISPRomics drug discovery engine have garnered significant interest in the medical sector. The technology can develop insights based on individual human genes on an industrial scale. Artificial intelligence (AI) and machine learning (ML) are establishing their presence in the genomics sector, especially in the field of genetic sequencing and analysis.

The human genome comprises over 3 billion base pairs, and this is the least of the complexities involved in the genomic study. The technological developments in genomics have significantly augmented the understanding of researchers. However, a comprehensive understanding of the genome remains far-fetched due to the sheer sophistication and immense information contained in the DNA.

Despite the advancements in the genomics sector, many functions of the genome remain a mystery. Scientists and researchers are still finding it challenging to decipher the genes and identify their role in human psychology and health. Several experts have proposed the utilization of robust AI systems to analyze the genomic data using deep learning technology. The incorporation of the latest AI-based technologies can potentially enable scientists in harnessing the vast troves of genomic data from multiple individuals, thus allowing them to forecast their regulatory functions and classify the mutation types.

AI technology will enable researchers to consolidate the vast troves of data and draw analytics from diverse datasets. It will empower them to map and decode the functions of genes and forecast the effect of genetic mutations on the overall genome. By integrating and processing the genomic, health, and lifestyle data of patients, the AI systems can draw inference regarding the unique conditions of individuals.

The AI systems can significantly accelerate the sequencing and analysis of DNA, while also ensuring cost-effectiveness and accuracy. It has enabled researchers to gain perspective on specific genetic blueprints which inform the activities of the organism. The insights facilitated by the systems bolster the decisions of researchers, helping them predict the diseases caused by mutations and form strategies to avert the adverse effects.

Researchers are also exploring the potential of deep learning, a branch of ML which leverages neural networks to extract insights from the input datasets. The technology has the capability of processing vast datasets in a short period. The availability of various datasets, including DNA, RNA, methylation, chromatin accessibility, histone modifications, chromosome interactions, and so on can facilitate the development of accurate prediction models designed for gene expression, genomic regulation, and variant interpretation.

As the availability of data increases, researchers and scientists can train and create enhanced models, promoting more precise and accurate predictions regarding genomic features and functions. Although it is in a stage of inception, the utilization of deep learning in genomics can pave the way for enhanced clinical genetics, crop improvement, population genetics, cancer treatment, and functional genomics.

Genetics are responsible for multiple factors affecting living organisms, are the primary determinants of the disposition toward several ailments, including cystic fibrosis, Huntington’s disease, sickle cell anemia, and many others. AI systems will enable scientists to comprehend the genetic makeup of organisms. Also, the integration of ML systems can significantly enhance the accuracy and efficiency of gene editing and gene sequencing operations.

The insights facilitated by the AI systems will enable genetic scientist to study the evolution of the human genome, which will potentially lead to personalized health predictions and disease susceptibilities. The data will empower health providers to tailor their treatments for their patients.

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