Nanqing Dong

DeepD is a genome foundation model that enables accurate sequence-to-function prediction and controllable genome-scale sequence generation across domains of life.

EssTFNet predicts human essential genes using a deep learning framework combining adaptive time-frequency analysis and DNA language models, achieving high accuracy and interpretability.

MassNet provides a large-scale mass spectral corpus for proteomics, enabling AI-driven de novo peptide sequencing with improved performance over existing methods.

This paper introduces deepGPS, a deep generative model that predicts protein subcellular localization using both sequence and image data, providing textual and image outputs.

ProDMM is a multimodal sequence modeling framework that captures interdependencies between protein motifs, coding DNA, and regulatory elements to predict phenotypes and generate optimized regulatory sequences.

Genomics-FM is a foundation model that reduces reliance on labeled data and outperforms existing models in genome annotation, epigenomic prediction, and variant effect assessment.

π-PrimeNovo is a non-autoregressive deep learning model for de novo peptide sequencing that achieves higher accuracy and 69x faster performance than existing methods.