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Intro
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Al for Bio-Medical Sciences (AIMS) Lab
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Overview
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Published Al models detect COVID-19 in chest X-rays
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What was the training data for these published models?
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How can we test how robust the models are?
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How robust are the models?
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What is important for the model's predictions?
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Can we fix shortcut learning with improved data?
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Conclusions
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Acknowledgements
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Outline
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Explainable AI
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Examples
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Intuition
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The recipe
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Unified framework
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SHAP's ingredients
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Human-friendly
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Game-theoretic
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Information-theoretic
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Choosing optimal combinations is hard
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This is the perfect opportunity for predictive models
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In high stakes scenarios, models should be interpretable
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A simple fix: ensemble attributions
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Interpretability uncovers transcriptional programs
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Bringing Interpretable Models to Cancer Precision Medicine
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Contrastive Analysis
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Latent Variable Models
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VAE Model
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Problem
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Contrastive Latent Variable Model
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Background datasets
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Contrastive VAE
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Inspecting the salient latent values
Description:
Explore cutting-edge research in AI and machine learning applications for life sciences in this 53-minute Allen School Colloquium featuring the AIMS Research Group. Dive into recent advancements in explainable AI, computational biology, and medicine, including COVID-19 detection in medical imaging, synergistic drug combinations for cancer treatment, and contrastive latent variable modeling for biological discovery. Learn about the challenges and opportunities in integrating AI/ML with life sciences, from developing interpretable models to identifying causes and treatments for diseases like cancer and Alzheimer's. Gain insights from PhD students and researchers as they discuss their work on efficient algorithms, theoretical foundations, and practical applications of AI in high-stakes domains such as healthcare and precision medicine.

Integrating AI and Machine Learning in Life Sciences - Allen School Colloquia

Paul G. Allen School
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#Computer Science #Artificial Intelligence #Machine Learning #Science #Life Science #Drug Discovery #Health & Medicine #Health Care #Radiology #Medical Imaging #Data Science #Bioinformatics #Computational Biology #Medicine #Medical Research #Cancer Research #Explainable AI
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Intro