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Related Experiment Video

Updated: Jun 28, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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ISMI-VAE: A deep learning model for classifying disease cells using gene expression and SNV data.

Han Li1, Yitao Zhou1, Ningyuan Zhao2

  • 1Department of Automation, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China; Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Making, Xiamen university, Xiamen, 361000, China.

Computers in Biology and Medicine
|April 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces ISMI-VAE, a novel deep learning method for integrating single nucleotide variation (SNV) and gene expression data from single-cell RNA sequencing. It effectively identifies disease-causing genetic features in cancer and COVID-19.

Keywords:
Multimodal classificationSNVScRNA-seq

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single nucleotide variations (SNVs) are linked to diseases like cancer and COVID-19.
  • Single-cell RNA sequencing (scRNA-seq) generates both SNV and gene expression data.
  • Integrating multimodal scRNA-seq data remains a challenge.

Purpose of the Study:

  • To develop a method for integrating SNV and gene expression data from scRNA-seq.
  • To improve the classification of disease cells using multimodal data.
  • To identify disease-causing genetic features through interpretable analysis.

Main Methods:

  • Introduced Interpretable Single-cell Multimodal Data Integration Based on Variational Autoencoder (ISMI-VAE).
  • Utilized latent variable models and deep learning for multimodal data integration.
  • Incorporated an attention mechanism for feature importance analysis.

Main Results:

  • ISMI-VAE effectively integrates SNV and gene expression data.
  • The method demonstrates superior performance compared to baseline approaches.
  • Successfully identified disease-causing gene features in cancer and COVID-19 datasets.

Conclusions:

  • ISMI-VAE offers an effective and interpretable approach for multimodal single-cell data analysis.
  • The method advances the understanding of genetic contributions to diseases.
  • Enables more accurate disease cell classification and biomarker discovery.