#ai-genome-analysis

#ai-genome-analysis

Scientists in the laboratory of Rendong Yang, PhD, associate professor of Urology, have developed a new large language model that can interpret transcriptomic data in cancer cell lines more accurately than conventional approaches, as detailed in a recent study published in Nature Communications. Long-read RNA sequencing technologies have transformed transcriptomics research by detecting complex RNA splicing and gene fusion events that have often been missed by conventional short-read RNA-sequencing methods.

Biology is undergoing a transformation. After centuries of studying life as it evolves naturally, researchers are now using a combination of computation and genome engineering to intervene, generating new proteins and even whole bacteria from scratch. The use of artificial-intelligence tools to design biological components, an approach known as generative biology, is set to turbocharge this area of research. Just last year, scientists used AI-assisted design to produce artificial genes that can be expressed in mammalian cells.

Martschenko's argument is largely that genetic research and data have almost always been used thus far as a justification to further entrench extant social inequalities. But we know the solutions to many of the injustices in our world-trying to lift people out of poverty, for example-and we certainly don't need more genetic research to implement them. Trejo's point is largely that more information is generally better than less.