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This paper introduces CoDiffGRN, a co-evolutionary discrete diffusion framework for gene regulatory network inference, along with a new benchmark BEELINE-KGC for inductive evaluation. It achieves state-of-the-art performance in novel regulatory discovery.
This paper develops a typed interface correspondence between Gene Regulatory Networks and agentic software systems using polynomial functors and wiring diagrams, mapping five biological motifs to composable software design patterns to address reliability and security challenges in autonomous AI agents.
This paper introduces a framework called 'Language Game' that enables dialogue with non-neural biological systems (e.g., gene regulatory networks) by treating communication as a game where the system's internal dynamics are frozen as a reinforcement learning policy, and only linear interfaces are trained. The approach allows diverse systems to communicate fluently without altering their parameters.
This paper introduces a new paradigm for universal Gene Regulatory Network (GRN) inference using single-cell foundation models, proposing Virtual Value Perturbation and Gradient Trajectory methods to distill regulatory knowledge.