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Project: Machine Learning to Improve Targeted Cancer Therapy

Advances in genomics have created many new cancer therapies that target specific genetic or molecular features, raising the potential for personalized treatment that improves effectiveness and decreases side effects. However, the majority of patients treated with targeted therapies do not respond as predicted, and detailed patient genomic information is expensive to acquire.

This collaboration proposes to improve targeting of cancer therapies by developing new AI approaches that recommend the best treatment based on a combination of genetic and pathology data. Researchers will build new methods drawing upon computer vision and machine learning to gather essential contextual information about individual cancers from tumor samples, utilizing both genomic and image-based features. This work will advance both artificial intelligence and cancer-focused data science by developing innovative solutions to emerging machine learning problems in cancer research, ultimately benefiting patients through more targeted, effective treatments.

Mentors: Samantha Riesenfeld and Alex Pearson

Samantha Riesenfeld is an Assistant Professor of Molecular Engineering and of Genetic Medicine, a member of the Committee on Immunology, an Associate Member of the Comprehensive Cancer Center, and co-director of the new Computational and Systems Immunology PhD track in Immunology and Molecular Engineering. She leads an interdisciplinary research program focused on developing and applying genomics-based machine learning approaches to investigate the cellular components, transcriptional circuitry, and dynamics underlying complex biological systems, with a special interest in inflammatory immune responses and solid tumor cancer.