Thousands of technology professionals from around the world recently gathered in Long Beach, California, for the sold-out Neural Information Processing Systems (NIPS) conference, a multi-track machine learning, artificial intelligence, and computational neuroscience event featuring talks, demonstrations, symposia and oral / poster presentations, along with several workshops.
Leaders in artificial intelligence, machine learning, and data engineering from Memorial Sloan Kettering attended NIPS and announced the winner of a unique competition created by MSK, Classifying Clinically Actionable Genetic Mutations.
The winning team — Xi Zhang, Dandi Chen, Yongjun Zhu, Chao Che, Chang Su, Sendong Zhao, Xu Min, and Fei Wang, from the Department of Healthcare Policy and Research at Weill Cornell Medical College — was awarded $10,000. The team’s solutions will be developed further at MSK.
The competition was conceived because precision medicine and genetic testing are disrupting the way diseases like cancer are treated. But a great deal of manual work is still involved, especially because once sequenced, a tumor can have thousands of genetic mutations. The challenge is in distinguishing mutations that contribute to tumor growth (drivers) from neutral ones (passengers). Currently, this interpretation is a lengthy, labor-intensive one. But the purpose of MSK’s unique competition is change that. More than 1,300 teams answered MSK’s call to develop a machine learning algorithm that, using MSK’s database, can begin to help automatically classify actionable genetic variations.
MSK is continuing and strengthening its focus on these emerging technologies and the talented individuals driving them, to help clinicians diagnose, treat, and find cures for cancer.
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