Yu Zhang

JOnTADS identifies TADs and stripes in Hi-C data across different experimental contexts, improving robustness and computational efficiency.

The paper examines chromatin structural changes and epigenetic reprogramming during mouse and human embryonic development, showing domain segregation correlates with genetic and epigenetic changes.

IAV-CNN predicts influenza A virus antigenic variants using a 2D convolutional neural network with a new amino acid representation, achieving state-of-the-art performance on three datasets.

ChINN predicts chromatin interactions from DNA sequences, showing good performance across samples and identifying sequence features associated with interactions in CLL patient data.

The paper integrates epigenomic and transcriptomic data from mouse hematopoiesis to identify regulatory elements and their dynamics across cell types.

OnTAD identifies hierarchical TAD structures from Hi-C data with improved accuracy and speed, revealing new insights into TAD organization and gene regulation.

This paper presents an improved functional annotation map for 127 human cell types using the IDEAS method, showing higher accuracy than the Roadmap Epigenomics consortium's map.