Multi-task learning from multimodal single-cell omics with Matilda

使用Matilda进行多模态单细胞组学的多任务学习.

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease

单细胞多组学整合的无监督神经网络.

Con-AAE: contrastive cycle adversarial autoencoders for single-cell multi-omics alignment and integration

Con-AAE：单细胞多组学对齐与整合的对比循环对抗自编码器.

Effective and scalable single-cell data alignment with non-linear canonical correlation analysis

通过非线性典型相关性分析进行有效可扩展的单细胞对齐.

scNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization

scNCL：通过邻域对比正则化从RNA迁移标签到ATAC.

scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

scCross：一种用于将单细胞多组学与无缝整合、跨模态生成和计算机模拟探索相结合的深度生成模型.