HOW THE INTEGRATION OF ARTIFICIAL INTELLIGENCE WILL REVOLUTIONISE GENETIC RESEARCH

Uddayvir Singh

In today’s era of constant and rapid technological advancement, we cannot for any longer treat the mammoth that is artificial intelligence as something that belongs to the future. AI is already impacting several different areas of our world and is already changing the face of various industries. One of these many areas is the study of genetics.

Ground-breaking findings

Recently, in the month of May 2023, research from University of California San Diego, revealed that machine learning AI is ‘revolutionising gene study’. This AI discovered rare “synthetic extreme” DNA sequences. The researchers at UC San Diego tested around 50 million DNA sequences with machine learning AI and found several synthetic extreme strands.

Synthetic extreme DNA sequences are sequences that can silence or activate genes. These are crucial not only because of their potential, but simply because they help deepen our fundamental understanding of DNA. Until now these were extremely difficult to find and isolate. However, this breakthrough has proven that as AI develops and learns, it can find these sequences far quicker and easier than we could.

These findings are very valuable not only to genetics, but to fields such as medicine and biotechnology, as with these findings we could test the effectiveness of one drug against another, instead of just testing one human’s genomes to another.

Its uses in the analysis of genomic sequences

Whilst at its current stage of ability artificial intelligence cannot be completely independent with most facets involved in genetics and genetic research, it is able to make the jobs of geneticists easier and more efficient.

For one, AI has been implemented to analyse genomic sequences. What would usually take researchers days and maybe even weeks to go through thoroughly, artificial intelligence programs can do in a few hours. Being able to scour and analyse these sequences allows researchers to be able to spend more time researching, and less time on more mundane tasks like the analysis of genomic sequences.

Additionally, this allows for a better and more accurate prediction of likely unintended genetic mutations that may occur in someone’s DNA. With more thorough, more accurate and quicker analysis of genomic sequences, researchers can then create specific prevention and treatment plans for patients at risk. These treatments could be specially tailored according to each patient’s genome, so as to maximise their efficiency.

This theory is already being used by researchers at University College London, and institutes in places like Abu Dhabi are also preparing themselves to begin similar practices in the near future.

DeepVariant, a program created by Google AI, uses a deep neural network to summon genetic variants from next generation DNA sequencing data.

How AI’s uses can be extended to other areas as it develops:

As this technology becomes more potent however, its uses can extend further than just analysing sequences. Indeed, looking at the rapid rate of development that AI programs are being put through, they can begin to be implemented in areas like genetic counselling too.

The technology can be utilised to go through a patient’s history (medical, social, familial, and personal), which would save counsellors a lot of time, and allow them to have more time to screen their patients for risk assessment. This would help them focus more of their time on patients that needed it more, whilst also ensuring that the other patients aren’t neglected.

Additionally, AI can help analyse a patient’s genetic test results, allowing them to be presented to counsellors in a clear, simple, and straightforward format. This further adds efficiency to the process and allows for the counsellors to get an even stronger understanding of their patient.

Then, while the counsellors can focus on the interactions with patients, the AI can complete the less interactive areas of the treatment. They can, for example, create specific and in-depth treatment plans for patients, that are maximised in terms of efficiency and effectiveness. This allows the counsellors to focus on the more complex aspects of the treatment process.

There are several other benefits to these shifts and implementations of AI within genetic counselling. The more efficient methods would help reduce costs. This could benefit patients too as reduced costs would also reduce prices for treatment, making it more accessible for more people, allowing for a larger proportion of the population to receive the higher quality care.

As this AI develops further, they could provide specific educational and even psychosocial support. This could entail dealing with difficult diagnoses and would mitigate the chances of human error on these aspects, as, while rare, even medical professionals make mistakes, hence increasing the discomfort of patients.

Hence, AI really does have the potential to revolutionise the genetics/genomics sector, amongst others. The question is whether institutes and individuals are ready to embrace these new methods, whilst maintaining their values and intellectuality.