Cheryl A. Telmer

An Open-Set Semi-Supervised Multi-Task Learning Framework for Context Classification in Biomedical Texts

The paper introduces CELESTA, a framework for biomedical context classification that improves accuracy by integrating semi-supervised multi-task learning and a large-scale annotated dataset.

The BioRECIPE Knowledge Representation Format

The paper introduces the BioRECIPE format for representing biological models to enable human-machine interaction and model development.

VIOLIN: A modular framework for scalable reconciliation of heterogeneous interaction graphs

VIOLIN reconciles heterogeneous interaction graphs with extracted molecular interactions, showing stable and interpretable results across different models and datasets.

Context-aware knowledge selection and reliable model recommendation with ACCORDION

ACCORDION selects relevant knowledge and recommends models by integrating literature retrieval, graph algorithms, and simulation, improving accuracy and reducing errors in biological systems.

Computational modeling of cell signaling and mutations in pancreatic cancer

The paper develops a computational model of pancreatic cancer signaling and mutations to identify potential inhibitor combinations for therapy.

Quantitative synapse analysis for cell-type specific connectomics

The paper develops genetically-encoded reagents for cell-type specific synapse labeling and connectivity analysis in mouse brain tissue, revealing PV inputs as the dominant inhibitory source onto L2 pyramidal neurons.