Researchers meld AI and genomics to find thousands of new viruses

Context: For most
of modern history, people have overlooked viruses, even though they are the
most abundant biological entity on the planet and carry immense ecological
significance. Viruses are found in every nook and corner of the world — from
soil and water to the atmosphere and even extreme environments like hot springs
and hydrothermal vents. Computational algorithms look for proteins similar to
sequences in databases. As a result, they risk missing proteins that have
evolved and changed. This risk may not hold for long, however, thanks to
scientific approaches that combine genetics, gene-sequencing, and deep-learning
methods it is now possible to detect them.

Key
points

·      
Overview: Understanding
viruses is crucial to anticipating emerging diseases. Studies estimate there
are around 300,000 mammalian viruses yet to be discovered. Unlike microbes,
viruses have remained understudied.

·      
Viruses: Viruses
are obligate parasites; they require a host to infect and replicate. This
relationship goes both ways. Thanks to advances in research, scientists are
increasingly recognising viruses as agents of disease but also as integral
components of ecosystems. Viruses drive genetic evolution through horizontal
gene transfer, control microbial population balance, and even affect
biogeochemical cycles.

Role- They plays critical roles in maintaining biodiversity
and may even influence climate regulation. Understanding their influence is
thus key to unravelling the complexities of life on earth. Yet only a small
fraction of the roughly 100 million to a trillion viral species has been
identified to date.

·      
Generative
AI (gen AI): Gen AI typically uses deep learning tools to create
content, such as images, text (including the familiar Large Language Models,
LLMs), or even music. Unlike traditional ML models, which make classifications
or predictions based on existing data, generative AI is designed to create
‘new’ data similar to the data it was trained on.

·      
Use of AI
in genomics: AI is valuable in genomics because it enables
researchers to analyse vast amounts of complex genomic data more efficiently
and accurately than before. For example, each human genome contains around 3
billion base pairs. Large-scale studies can involve thousands of different
genomes, each containing billions of letters of DNA code, meaning that
comparing the letters to find patterns difficult. AI can also help identify
patterns and correlations in data that are too subtle or complex for simple
analysis to detect. They can also predict the impact of specific changes. But
there is a risk of false positives (identifying patterns that are not correct),
which means lots of data is required to gain confidence in any findings.

Way
forward- Generative AI could enhance much of the scientific
understanding of genomics, including genetic variation, how mutations affect
DNA function, and even how to create new genetic sequences. This may bring us
closer to personalised medicine. Scientists will apply AI across multiple data
types to gain a more thorough understanding of biological processes.

·      
Artificial
intelligence for virus detection and diagnosis: AI methods
can be very useful in building detection system for viruses when there is too
much of data which cannot be easily handled by the humans.

Example- A scanning
system used at airport to scan multiple faces continuously. When trained with
some data about the features of viruses, methods of AI can identify people who
are sick without fevers. Many researchers and scientists are working on
different methods like ANNs, machine learning, and deep learning that can
analyse available big data to detect signs of illness before human can detect
with better accuracy and efficiency.