
Erica Brown

I’m a rising junior at Brown University, concentrating in Applied Mathematics-Biology. My research interests primarily surround the applications of machine learning to precision medicine
Predicting cell-type-specific responses to chemical perturbations assists targeted treatment development with minimal off-target effects. Experiments profile cells’ transcriptomic responses to various perturbations, however, scaling them to numerous cell types and perturbations is costly. Working at Broad has introduced me to some of the most intelligent and inspiring people that I’ve ever met. This summer has strongly developed my passion for scientific research, and I know that I’ll carry what I’ve learned from my mentors here for the rest of my career.Computational methods can predict cellular responses in unmeasured contexts, but this requires considering cell-type-specific gene programs. Single-cell foundation models adapt large language models to transcriptomic data, aiming to learn these programs for use in low-data settings.
We evaluate the utility of gene embeddings for generalizing chemical perturbation effect predictions to unseen cell types. Using MIX-Seq data on cancer cell lines, we use gene embeddings in a metric learning framework to (1) predict transcriptomic responses to perturbations and (2) classify chemicals by their mechanisms of action. We compare their performance with empirical gene co-expression data and metric learning-derived gene relationships using cell-line-specific training data. Overall, while embeddings are more informative than co-expression data, they fall short of the performance achieved with cell-line-specific training data
Project: Evaluating Single-Cell Embeddings for Chemical Perturbation Response and Mechanism of Action Prediction
Mentor: Pinar Demetci, Schmidt Center