.png){kind=icon}
Return
Interpretable multi-modal data integration
.png){kind=icon}
PRE
AI
DOI：10.1038/s43588-021-00186-w.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
OMICS
Journal
IF:
18.3
Papers:
3.1K
Citations:
4.0K
Organization
Cited Papers
MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics.png)
GENOME BIOLOGY
OAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveShareSaveA deep manifold-regularized learning model for improving phenotype prediction from multi-modal data
OAAI
OAAIShareSaveManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networksBMC GENOMICSOAAI
BMC GENOMICSIF3.7
OAAIShareSaveMultimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seqNATURE PROTOCOLSOAAI
NATURE PROTOCOLSIF16
OAAIShareSaveShareSaveShareSavescTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell DataPATTERNSOAAI
PATTERNSIF7.4
OAAIShareSaveShareSave no more
no more