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Computational foundations of morphogenomics

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Genomic data are available at unprecedented scales due to developments in high throughput sequencing and imaging technologies. In parallel, innovations in statistics and machine learning boast successful algorithms for a wide array of engineering applications. However, bridging these two worlds—the world of real, messy biological data, and that of algorithms and computation—remains challenging. In this talk, I will review recent attempts to extract interpretable representations in genomics and imaging studies. I will discuss pitfalls and formulate open questions with an emphasis on statistical challenges arising from both data collection and data analysis, with the final goal of building a foundation for and testing the limits of computational morphogenomics – the attempt to determine shape, form and trajectory from genomic information.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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