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1
Introduction
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Engineering matters
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Generative modeling
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Data integration
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Representation learning
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Integrated lung cell atlas
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Learning distribution
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Population level integration
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Scale
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Map phenotype
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Multigrade
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Experimental design
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Experimental results
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Adding priors
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Differential programming
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Multiomics
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Multiomics example
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Prediction human
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Embedding cells
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Munich
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Questions
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Encoder
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Question
Description:
Explore a comprehensive lecture on generative AI applications in single-cell response modeling presented by Fabian Theis at the EWSC-MIT EECS Joint Colloquium Series. Delve into topics such as engineering considerations, generative modeling techniques, data integration strategies, and representation learning. Examine the creation of an integrated lung cell atlas and learn about distribution learning, population-level integration, and phenotype mapping. Investigate experimental design, results analysis, and the incorporation of priors in modeling. Discover the potential of differential programming and multiomics approaches, including practical examples and human prediction models. Gain insights into cell embedding techniques and engage with a Q&A session covering various aspects of the presented research.

Generative AI for Modeling Single-Cell Responses

Broad Institute
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