Yiqun T. Chen

Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models

This paper benchmarks differential expression classification in single-cell perturbation models, finding that simple baselines often outperform deep learning models.

Mind the Alignment Gap: A Spatial Transcriptomics Benchmark for Scientific Coding Agents

The paper introduces a benchmark for scientific coding agents in spatial transcriptomics alignment, showing that richer context improves tool exploration but reduces alignment accuracy.

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis

scELMo uses Large Language Models to analyze single-cell data for clustering, batch correction, and cell-type annotation without retraining, outperforming existing methods.

RL-Finetuning of OpenAI o1-mini to Enhance Biomedical Reasoning

This study evaluates the effectiveness of RL finetuning o1-mini for biomedical reasoning tasks and finds mixed results in gene classification and small molecule prediction.

GenePert: Leveraging GenePT Embeddings for Gene Perturbation Prediction

GenePert uses GenePT embeddings from ChatGPT to predict gene expression changes from perturbations, outperforming existing models across multiple datasets.

GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT

GenePT embeds National Center for Biotechnology Information (NCBI) gene text descriptions using GPT-3.5 and aggregates them by expression to represent single cells without pretraining on millions of transcriptomes.