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David van Dijk
Assistant Professor,
Dept. of Computer Science & Dept. of Int. Medicine,
Yale University
Meeting: Single-cell analysis in the age of LLMs

In this talk, I will argue that biology itself operates like a language, where systems like the immune response communicate through combinatorial interactions, much like words forming sentences. I will present recent work from our lab, starting with CINEMA-OT, a causal inference method applied to combinatorial cytokine stimulation, revealing nonlinear interactions between cytokines. I will then focus on Cell2Sentence, a project that transforms single-cell data into 'cell sentences' to train LLMs for generating and predicting cellular behaviors. Finally, I will briefly discuss CaLMFlow, where LLMs are adapted to model continuous systems, highlighting their versatility beyond discrete language tasks. Together, these projects illustrate how LLMs are advancing single-cell analysis and biological research.

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Syed Rizvi
Ph.D. student
Department of Computer Science
Yale University
Primer: Large Language Models and Biological Foundation Models

In the primer part of the seminar, we will explore Large Language Models (LLMs) and biological foundation models, covering their architecture, training, and how they are being adapted to analyze complex biological data, such as single-cell genomics. This section will introduce the idea that these models can help us decode the 'language' of biology.

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