Revealing cell-level gene regulation through integration of single-cell multi-omics measurements

通过整合单细胞多组学测量揭示细胞水平的基因调控

• 批准号: 10810174
• 负责人: Yijie Wang
• 金额: $ 0.64万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-26 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10810174
• 关键词: Benchmarking; Cells; Computing Methodologies; Foundations; Gene Expression Regulation; Measurement; Mentors; Messenger RNA; MicroRNAs; Neural Network Simulation; Organism; Post-Transcriptional Regulation; Prevention; Process; Regulation; Research; Solid; disease diagnosis; insight; multiple omics

Summary We propose to use a visible neural network (VNN) model to identify miRNA-mRNA interactions at the single-cell level. We will first compare and benchmark the two types of VNN models and find the best VNN model. Next, we will incorporate the best VNN model into our VNN model to identify miRNA-mRNA interactions by integrating single-cell multi-omics measurements. We expect to develop computational methods to understand better miRNA-mRNA regulation, which would lay a solid foundation for disease diagnosis, treatment, and prevention.

总结 我们建议使用可视神经网络（VNN）模型来识别miRNA-mRNA在单细胞上的相互作用， 水平我们将首先对两种类型的VNN模型进行比较和基准测试，并找到最好的VNN模型。接下来我们就 将最好的VNN模型整合到我们的VNN模型中，通过整合单细胞 多组学测量。我们期望开发计算方法来更好地理解miRNA-mRNA 监管，这将为疾病的诊断，治疗和预防奠定坚实的基础。

项目成果

AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency.

AIscEA：通过其生物一致性对单细胞基因表达和染色质可及性进行无监督整合。

• DOI: 10.1093/bioinformatics/btac683
• 发表时间: 2022
• 期刊: Bioinformatics (Oxford, England)
• 作者: Jafari,Elham;Johnson,Travis;Wang,Yue;Liu,Yunlong;Huang,Kun;Wang,Yijie
• 通讯作者: Wang,Yijie