Xiangchun Li

Nov 5, 2024 | pubs.acs.org | Xiaowei Li |Xiangchun Li |Zhifeng Chen |Jianhua Zeng

Nov 5, 2024 | dx.doi.org | Xiaowei Li |Xiangchun Li |Zhifeng Chen |Jianhua Zeng

Jan 15, 2024 | biorxiv.org | Xilin Shen |Xiangchun Li

AbstractFoundation models have demonstrated exceptional efficacy across diverse downstream tasks. However, within the realms of genomics and transcriptomics, a notable gap persists in the availability of models that afford a comprehensive understanding of nucleotide sequence principles across various species. Here, we present OmniNA, a foundation generative model designed for comprehensive nucleotide sequence learning.

Jan 15, 2024 | biorxiv.org | Xilin Shen |Xiangchun Li

AbstractProtein-RNA interactions play an essential role in the regulation of transcription, translation, and metabolism of cellular RNA. Here, we develop Reformer, a deep learning model that predicts protein-RNA binding affinity purely from sequence. We developed Reformer with 155 RNA binding protein (RBP) targets from 3 cell lines. Reformer achieved high prediction accuracy at single-base resolution when tasking with inferring protein- and cell-type-specific binding affinity.