most citedSynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2026

MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Prediction

Marc Boubnovski Martell, Josefa Lia Stoisser, Lawrence Phillips +6

Predicting transcriptional responses to unseen genetic perturbations is essential for understanding gene regulation and prioritizing large-scale perturbation experiments. Existing…

cs.AI2025

Towards Label-Free Biological Reasoning Synthetic Dataset Creation via Uncertainty Filtering

Josefa Lia Stoisser, Lawrence Phillips, Aditya Misra +5

Synthetic chain-of-thought (CoT) traces are widely used to train large reasoning models (LRMs), improving generalization by providing step-level supervision. Yet most approaches re…

cs.AI2025

Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning

Josefa Lia Stoisser, Marc Boubnovski Martell, Lawrence Phillips +6

Large language model (LLM) agents are increasingly deployed in structured biomedical data environments, yet they often produce fluent but overconfident outputs when reasoning over…

cs.AI2025★ 1 cited

SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction

Lawrence Phillips, Marc Boubnovski Martell, Aditya Misra +4

Predicting cellular responses to genetic perturbations represents a fundamental challenge in systems biology, critical for advancing therapeutic discovery and virtual cell modeling…

q-bio.GN2025

Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations

Kalyan Ramakrishnan, Jonathan G. Hedley, Sisi Qu +5

We train a neural network to predict distributional responses in gene expression following genetic perturbations. This is an essential task in early-stage drug discovery, where suc…

cs.DB2025

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries

Josefa Lia Stoisser, Marc Boubnovski Martell, Kaspar Märtens +4

Electronic health records (EHRs) contain richly structured, longitudinal data essential for predictive modeling, yet stringent privacy regulations (e.g., HIPAA, GDPR) often restric…