Generative artificial intelligence, like the kind that powers OpenAI’s DALL-E, ChatGPT, and other popular programs, is going to be an important tool for breakthroughs in oncology, the study of cancer, according to Daphne Koller. Koller is an AI pioneer and co-founder and CEO of life sciences AI firm Insitro.
“What we’ve taken on as an effort is to really learn the language of histopathology [the study of tissues]… and then use that to […] give us potential [drug] targets,” said Koller, speaking at a daylong workshop hosted by Stanford University’s Human-Centered AI institute on Tuesday, titled, “New Horizons in Generative AI: Science, Creativity, and Society.”
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Koller is an adjunct professor of computer science at Stanford. Koller explained a two-step process that can lead to novel drug targets for cancer.
In the first step, Insitro machine learning AI technology is able to analyze images of cancerous tissue, a histology image generated from a biopsy. A human pathologist will “typically boil down these images of billions of pixels into, like, three numbers,” she explained, “And it’s clear that there is a ton more information that is available within them” that is not being used.
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By using machine learning, the computer will “really learn the language of histopathology,” she said, which in turn lets the machine predict genetic changes in patients with cancer with 90% to 95% accuracy.
“So, basically, by looking at a slide, you can say this patient has this genetic mutation versus this other patient, something that no clinician can really do,” she explained.
That’s the first step. To find drug targets, you need a lot more samples of tissue than are actually collected — thousands versus dozens. To solve that supply of images, the Insitro team used generative AI to create “deep fakes” of tissue images, said Koller. “Rather than generating images of movie stars, we generate images of pathology slides.”
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By multiplying tissue samples from hundreds to thousands, Koller explained, a much larger sample can be analyzed using a special tool developed at Stanford called an “ATAC-seq” assay. The team…