Zhenghao Zhang

This study evaluates how large language models perform in-class learning for antibody classification using biological sequences, finding that few-shot prompting with diverse demonstrations achieves competitive results.

This paper introduces a framework that guides dimensionality reduction using knowledge-derived genes to recover rare but important signals in single-cell data, demonstrating its effectiveness in identifying rare cell subtypes.

The paper evaluates 10 software tools for computing AUPRC and finds they produce conflicting and overly-optimistic results.