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Unsupervised classification of multi-omics data during cardiac remodeling using deep learning.

Neo Christopher Chung1, Bilal Mirza2, Howard Choi3

  • 1NIH BD2K Center of Excellence for Biomedical Computing, University of California Los Angeles, Los Angeles, CA 90095, USA; Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.

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Summary
This summary is machine-generated.

Deep learning methods, including LSTM-VAE and DCEC, effectively classify temporal multi-omics data for cardiovascular disease research. These advanced techniques identified more significant biological pathways than traditional clustering, highlighting their potential for translational discoveries.

Keywords:
CardiovascularClusteringIntegrative analysisMulti-omicsTime-seriesUnsupervised deep learning

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Area of Science:

  • Cardiovascular Diseases
  • Computational Biology
  • Bioinformatics

Background:

  • Multi-omics data integration offers potential for cardiovascular disease (CVD) translational discoveries by analyzing molecular abundance over time.
  • Effective integrative analysis of temporal multi-omics requires computational methods that handle data heterogeneity and complexity.

Purpose of the Study:

  • To apply and evaluate innovative deep learning (DL) approaches for unsupervised classification of temporal multi-omics data in cardiac remodeling.
  • To compare the performance of DL methods against conventional clustering algorithms in identifying biological pathways.

Main Methods:

  • Utilized long short-term memory-based variational autoencoder (LSTM-VAE) for time-series numeric data and deep convolutional embedded clustering (DCEC) for temporal trend images.
  • Performed joint optimization for image reconstruction and cluster assignment with DCEC, contrasting with a two-step procedure for LSTM-VAE.
  • Included K-means, partitioning around medoids (PAM), and hierarchical clustering as conventional comparison methods.

Main Results:

  • Deep learning methods, particularly DCEC, identified a higher number of significant biological pathways compared to conventional clustering algorithms.
  • DCEC demonstrated superior performance, likely due to its unified framework leveraging visual similarities in temporal trends.
  • Pathway enrichment analysis using Reactome knowledgebase confirmed the efficacy of DL approaches.

Conclusions:

  • Unsupervised deep learning, especially DCEC, shows significant promise as an analytical approach for the integrative analysis of temporal multi-omics data.
  • These findings suggest DL methods can enhance the discovery of biological interactions and networks in complex diseases like CVDs.
  • The study advocates for the adoption of DL techniques to advance translational research in cardiovascular medicine.