Today we’re joined by Su-In Lee, a professor at the Paul G. Allen School of Computer Science And Engineering at the University Of Washington. In our conversation, Su-In details her talk from the ICML 2023 Workshop on Computational Biology which focuses on developing explainable AI techniques for the computational biology and clinical medicine fields. Su-In discussed the importance of explainable AI contributing to feature collaboration, the robustness of different explainability approaches, and the need for interdisciplinary collaboration between the computer science, biology, and medical fields. We also explore her recent paper on the use of drug combination therapy, challenges with handling biomedical data, and how they aim to make meaningful contributions to the healthcare industry by aiding in cause identification and treatments for Cancer and Alzheimer's diseases.

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📖 CHAPTERS
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00:00 - Background
04:40 - Collaboration between computer science, biology, and medicine
07:23 - Explainable AI contributes to feature collaboration
14:04 - Foundational AI methods
16:04 - Computational challenges and approaches
19:52 - Removal-based approaches are more robust but computationally intensive
22:26 - Overview, challenges, and successes of drug combination therapy
30:47 - Computational biology: pathway, biological insights, flexibility algorithms
32:40 - Challenges with handling biomedical data
35:23 - Research lab’s future goals


🔗 LINKS & RESOURCES
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Explainable AI: Where we are and how to move forward for biology and health - [ Ссылка ]
Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models - [ Ссылка ]

For a complete list of references, head over to [ Ссылка ].


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