报告人：苑秋月（克莱姆大学）

邀请人：邹猛

报告时间：2024年1月28日（星期日）10:00-12:00

报告地点：东三十二楼115室

报告题目：Lifelong Learning for Gene Regulation Modeling from Single-Cell Multiome Data

报告摘要：We present LINGER (LIfelong neural Network for GEne Regulation), a novel neural network-based method to infer gene regulatory networks (GRNs) from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 4-7 fold relative increase in accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

报告人简介：苑秋月，克莱姆森大学博士后研究员, 研究方向为数据科学、基因调控网络、精准医学、人工智能。在国际期刊发表9篇论文，期刊包括Genome Biology，Cell Reports Medicine，Nature Communications，另有一篇Nature Biotechnology (Under revision following positive peer reviews)。