Xiang Lin

2 months ago | biorxiv.org | Guadalupe Gonzalez |Guadalupe E González |Xiang Lin |Isuru Herath |Kirill Veselkov

AbstractPhenotype-driven approaches identify disease-counteracting compounds by analyzing the phenotypic signatures that distinguish diseased from healthy states. These approaches can guide the discovery of targeted perturbations, including small-molecule drugs and genetic interventions, that modulate disease phenotypes toward healthier states.

Oct 7, 2024 | biorxiv.org | Guadalupe Gonzalez |Guadalupe E González |Xiang Lin |Isuru Herath |Kirill Veselkov

AbstractAs an alternative to target-driven drug discovery, phenotype-driven approaches identify compounds that counteract the overall disease effects by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network (GNN) designed to predict combinatorial perturbagens - sets of therapeutic targets - capable of reversing disease effects.

May 23, 2024 | nature.com | Tian Tian |Xiang Lin |Zhi Wei

AbstractSpatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations.