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Two scientific leaps, in machine learning algorithms and powerful biological imaging and sequencing tools , are increasingly being combined to spur progress in understanding diseases and advance AI itself.
Cutting-edge, machine-learning techniques are increasingly being adapted and applied to biological data, including for COVID-19.
Recently, researchers reported using a new technique to figure out how genes are expressed in individual cells and how those cells interact in people who had died with Alzheimer's disease.
Machine-learning algorithms can also be used to compare the expression of genes in cells infected with SARS-CoV-2 to cells treated with thousands of different drugs in order to try to computationally predict drugs that might inhibit the virus.
While, Algorithmic results alone don't prove the drugs are potent enough to be clinically effective. But they can help identify future targets for antivirals or they could reveal a protein researchers didn't know was important for SARS-CoV-2, providing new insight on the biology of the virus
read the original article which speaks about a lot more at https://www.axios.com/ai-machine-learning-biology-drug-development-b51d18f1-7487-400e-8e33-e6b72bd5cfad.html
Via nrip
The insight in this article is shared among a number of early adopters and tinkerers in the Healthcare ML space. A number of specific problems which are being worked on within the Machine learning space which relate to life sciences are stimulants which help us advance the science of machine learning much faster than other areas.
This is because the science of Biology requires more than patterns being found and re-applied to identify something. It requires understanding the interaction of all the contributing factors behind that pattern being created in the first place. So, creating a drug to target a protein involved in a disease does require understanding how the genes that give rise to that protein are regulated.