.png){kind=icon}
返回
Interpretable multi-modal data integration
.png){kind=icon}
PRE
AI
DOI：10.1038/s43588-021-00186-w.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Integrating multi-modal features is challenging due to the differences in the underlying distributions of each data type and the nonlinear associations across modalities. The deepManReg model improves the identification and interpretability of associations between modalities defining complex phenotypes.
Keyword:
OMICS
期刊
IF:
18.3
论文数:
3.1K
被引数:
4.0K
机构
引用论文
MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics.png)
GENOME BIOLOGY
OAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data
OAAI
OAAI分享收藏ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networksBMC GENOMICSOAAI
BMC GENOMICSIF3.7
OAAI分享收藏Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seqNATURE PROTOCOLSOAAI
NATURE PROTOCOLSIF16
OAAI分享收藏scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell DataPATTERNSOAAI
PATTERNSIF7.4
OAAI分享收藏 没有更多内容
没有更多内容