Machine learning can analyze photographs of cancer, tumor pathology slides, and genomes. Now, scientists are poised to integrate that information into cancer uber-models.
Deep learning approaches in development by big players in the tech industry can be used by biologists to extract more information from the images they create.
The Carnegie Mellon computational biologist thinks machine learning algorithms can direct high-throughput experiments to solve the field’s unanswered questions.
The latest machine learning models can identify many visual and molecular features of a particular cancer. If the technology advances to the clinic, it could help diagnose patients and predict survival.
Double-strand breaks can produce several different outcomes.
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