Qinhu Zhang

Hierarchical refinements of cis-regulatory inputs improve scalable gene expression prediction

The paper introduces a two-stage framework for gene expression prediction from cis-regulatory elements, showing improved performance across human cell types and tissues.

A scalable computational framework for predicting gene expression from candidate cis-regulatory elements

This paper introduces ScPGE, a framework predicting gene expression from cis-regulatory elements using convolutional neural networks and transformers, showing improved accuracy in identifying enhancer-gene interactions.

Cross-species prediction of transcription factor binding by adversarial training of a novel nucleotide-level deep neural network

This study introduces a novel deep neural network for cross-species TF binding prediction and improves performance through adversarial training.

Computational prediction and characterization of cell-type-specific and shared binding sites

The paper proposes two computational methods to predict and characterize cell-type-specific and shared TF binding sites using XGBoost and CNN, showing improved performance over existing methods.

Base-resolution prediction of transcription factor binding signals by a deep learning framework

The paper introduces a deep learning framework called FCNsignal to predict TF-DNA binding signals at base-resolution, outperforming existing methods and demonstrating its ability to locate binding regions and predict motifs.