Machine learning is a branch of AI in which computer systems continuously improve and learn based on data and experience. In a new study, Kadonaga, Vo ngoc (former postdoctoral fellow at the University of California, San Diego, now Velia Therapeutics) and Rhyne (staff researcher) used a technique known as support vector regression to analyze the well-established 20 “Trained” a machine learning model on 10,000 DNA sequences. Based on real laboratory experimental data. These are the targets presented as examples for machine learning systems. He then “fed” 50 million test DNA sequences to human and Drosophila machine learning systems and compared the sequences to ask him to identify unique sequences within the two giant datasets. .
A machine learning system showed that the human and Drosophila sequences are nearly redundant, but the researchers found a rare instance in which gene activation was highly active in humans but not in Drosophila. We focused on the core question of whether AI models can discriminate. The answer was yes. Machine learning models have successfully identified human-specific (and Drosophila-specific) DNA sequences. Importantly, the functionality of the AI-predicted extreme sequences was validated in Kadonaga’s lab using conventional (wet lab) testing methods.
“Before undertaking this research, we did not know whether an AI model would be ‘intelligent’ enough to predict the activity of 50 million sequences, especially the ‘extreme’ sequences of outliers with unusual activity. . Therefore, it is very impressive and very noteworthy that the AI model was able to predict the activity of a rare extreme sequence of 1 in 1 million,” Kadonaga said, adding that the equivalent 100 million He added that it is inherently impossible to conduct wet-lab experiments of the machine learning technology analyzed because each wet-lab experiment takes nearly three weeks to complete.
The rare sequences identified by machine learning systems serve as successful demonstrations and set the stage for other uses of machine learning and other AI technologies in biology.
“In daily life, people are finding new applications for AI tools such as ChatGPT. Here we demonstrated the use of AI for designing customized DNA elements in gene activation. and biomedical research,” Kadonaga said. “In a broader sense, the biologist is probably just beginning to harness the power of AI technology.”
This study was supported by funding from the National Institutes of Health (R35 GM118060).
