Xilin Shen

Jan 17, 2025 | medrxiv.org | Corey Horien |Francesca Mandino |Abigail Greene |Xilin Shen

James C. McPartland consults or has consulted with Customer Value Partners, Bridgebio, Determined Health, Apple, and BlackThorn Therapeutics, has received research funding from Janssen Research and Development, serves on the Scientific Advisory Boards of Pastorus and Modern Clinics, and receives royalties from Guilford Press, Lambert, Oxford, and Springer. This work was supported by the National Institutes of Health (P50MH115716, T32GM007205 to CH and ASG, and TR001864 to ASG).

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.

Dec 16, 2023 | biorxiv.org | Francesca Mandino |Xilin Shen |Gabriel Desrosiers-Gregoire |David O'Connor

AbstractAmyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks.