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PENN: Phase Estimation Neural Network on Gene Expression Data.

Aram Ansary Ogholbake1, Qiang Cheng1

  • 1University of Kentucky, Lexington KY 40526, USA.

The 4Th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Joint International Conference on Deep Learning, Big Data and Blockchain (4Th : 2023 : Marrakech, Morocco ; Online)
|October 2, 2023
PubMed
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This study introduces a deep learning method to predict gene expression phases from untimed datasets. This approach helps uncover circadian gene patterns, offering insights into physiology and disease.

Area of Science:

  • Genomics
  • Computational Biology
  • Chronobiology

Background:

  • Transcriptomic data, such as gene expression, is rapidly expanding, necessitating advanced analytical techniques.
  • The Gene Expression Omnibus (GEO) contains millions of datasets, but many lack timestamps, impeding circadian gene analysis.
  • Understanding circadian gene patterns is crucial for insights into physiology, behavior, and disease.

Purpose of the Study:

  • To develop a novel deep learning approach for predicting the phases of circadian gene expression from untimed datasets.
  • To leverage periodic oscillation information within cyclic genes to improve phase estimation accuracy.
  • To enable the exploration of circadian gene behavior despite missing temporal information.

Main Methods:

  • A deep neural network architecture was designed to predict phases of un-timed gene expression samples.

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  • The objective function was engineered to incorporate and regulate phase estimation using inherent periodic oscillation information.
  • The method was validated using datasets from mouse heart, mouse liver, and human brain temporal cortex.

    • Main Results:

    • The proposed deep learning method effectively predicted phases for circadian genes in untimed datasets.
    • The approach successfully uncovered rhythmic patterns within the analyzed circadian gene expression data.
    • Experimental results demonstrated the method's capability in phase prediction and pattern discovery.

    Conclusions:

    • The developed deep learning approach provides a valuable tool for analyzing circadian gene expression from untimed data.
    • Accurate phase prediction facilitates a deeper understanding of circadian rhythms' role in biological processes and diseases.
    • This method enhances the utility of large-scale transcriptomic datasets like those in GEO for chronobiology research.