scCycleMol adds a learnable circular cell-cycle head with closed-loop supervision from predicted treated expression, yielding higher r-squared on SciPlex3 gene predictions and improved phase accuracy versus ChemCPA baselines.
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Chem2Gen-Bench is a new benchmark and evaluation framework for measuring alignment between chemical and genetic perturbation responses in matched cell-target contexts using retrieval, similarity, and embedding comparisons.
Wasserstein least squares extends Euclidean least squares to distribution-valued responses via convex analysis, yielding n^{-1/2} rates under template deformation and faster barycenter rates than prior work.
K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while improving downstream differential expression prediction.
COAST learns context-specific causal graphs and structural causal models from data, then uses constraint-aware multi-objective optimization to identify interventions that induce user-defined state transitions while balancing efficacy, complexity, and stability.
citing papers explorer
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Chem2Gen-Bench: Benchmarking Chemical-to-Genetic Translation in Perturbation Response Space
Chem2Gen-Bench is a new benchmark and evaluation framework for measuring alignment between chemical and genetic perturbation responses in matched cell-target contexts using retrieval, similarity, and embedding comparisons.
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Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors
K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while improving downstream differential expression prediction.
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Causal Intelligence for Constraint-Aware Intervention Design to Induce State Transitions
COAST learns context-specific causal graphs and structural causal models from data, then uses constraint-aware multi-objective optimization to identify interventions that induce user-defined state transitions while balancing efficacy, complexity, and stability.