DeepSomatic is an AI-powered tool that identifies cancer-related mutations in a tumor’s genetic sequence to help pinpoint what’s driving the cancer.
Cancer is fundamentally a genetic disease in which the genetic controls on cell division go awry. Many types of cancer exist, and each poses unique challenges as it can have distinct genetic underpinnings. A powerful way to study cancer, and a critical step toward creating a treatment plan, is to identify the genetic mutations in tumor cells. Indeed, clinicians will now often sequence the genomes of biopsied tumor cells to inform treatment plans that specifically disrupt how that cancer grows.
With partners at the University of California, Santa Cruz Genomics Institute and other federal and academic researchers, our new paper, “DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies” in Nature Biotechnology presents a tool that leverages machine learning to identify genetic variants in tumor cells more accurately than current methods. DeepSomatic is a flexible model that uses convolutional neural networks to identify tumor variants. It works on data from all major sequencing platforms, for different types of sample processing, and can extend its learning to cancer types not included in training.
We have made both the tool and the high-quality training dataset we created openly available to the research community. This work is part of broader Google efforts to develop AI methods to understand cancer and help scientists treat cancer, including analyzing mammogram images for breast cancer screening, CT scans for lung cancer screening, as well as a partnership aimed at using AI to advance research on gynecological cancers. Our hope is to speed cancer research and further the goal of precision medicine.
read more on Google Research Blog