Qin Cao

This paper introduces SHARP, a method to enhance Hi-C data resolution while avoiding artificial structures by decomposing signals and using multi-scale attention.

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 study identifies CDKL5-mediated phosphorylation of nELAVL as critical for regulating RNA metabolism and condensate communication, essential for visual cortex maturation.

The paper models transcript isoform expression levels using epigenomic data from 324 human samples, showing that a universal model outperforms tissue-specific models.

The paper investigates how three-dimensional chromatin domain structures influence gene regulation and finds that domain surfaces are enriched for regulatory elements and disease variants.

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

The paper analyzes transcriptome and methylome data from mouse oocytes to understand aging and maturation, identifying biomarkers and a predictive model for oocyte quality.

The paper develops a custom ENCODE annotation for cancer genomics, highlighting TF-RBP interactions and network rewiring during oncogenesis, and validates findings using experimental assays.