Decoding Disease Using AI to Unlock the Mysteries of SLC Transporters
Yun S. Song, Ph.D. Professor Electrical Engineering & Computer Sciences Statistics UC Berkeley
By Sarah Colwell
For decades, medical researchers have struggled to predict how genetic mutations affect human health, leaving clinicians without the tools they need to provide truly personalized care. Some mutations lead to devastating diseases, while others influence how an individual’s body metabolizes medications, making treatment outcomes unpredictable. Supported by UC Noyce Initiative, a team of researchers from UC Berkeley and UC San Francisco are now leveraging digital innovation to develop machine-learning models that are capable of predicting disease-related mutations, laying the groundwork for more efficient drug development, improved diagnostics and precision medicine. “This advance will help clinicians make more accurate diagnoses and guide the development of personalized treatment plans based on an individual’s genetic profile,” said Yun S. Song, Ph.D., principal investigator By integrating machine learning, computational biology, and deep mutational scanning, they are developing models that can predict the effects of genetic mutations with unprecedented accuracy. This research has the potential to revolutionize precision medicine, empowering clinicians with digital tools that enable tailored, data-driven treatments. A Deep Dive into Mutation Prediction To unravel the complexities of solute carrier transporters (SLC), the research team performed experiments on a key transporter involved in drug metabolism called organic cation transporter member 1.
Steven Brenner, Ph.D. Professor Bioengineering, Molecular and Cell Biology, and Plant and Microbial Biology UC Berkeley
Adjunct Professor UC San Francisco
Sook Wah Yee, Ph.D. Assistant Adjunct Professor Bioengineering and Therapeutic Sciences UC San Francisco
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COMPUTATIONAL HEALTH
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