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Epigenome-Wide Association Studies pp 201–210Cite as

Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction

Part of the Methods in Molecular Biology book series (MIMB,volume 2432)

Abstract

We introduce the CPFNN (Correlation Pre-Filtering Neural Network) for biological age prediction based on blood DNA methylation data. The model is built on 20,000 top correlated DNA methylation features and trained by 1810 healthy samples from GEO database. The input data format and the instructions for parser and CPFNN model are detailed in this chapter. Followed by two potential uses, age acceleration detection and unknown age prediction are discussed.

Key words

  • Machine learning
  • Neural networks
  • Aging
  • Prediction
  • DNA methylation

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Author information

Authors and Affiliations

  1. Department of Computer Science, Trinity University, San Antonio, TX, USA

    Lechuan Li, Chonghao Zhang, Hannah Guan & Yu Zhang

Authors
  1. Lechuan Li
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  2. Chonghao Zhang
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  3. Hannah Guan
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  4. Yu Zhang
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Corresponding author

Correspondence to Yu Zhang .

Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Ph.D. Weihua Guan

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Li, L., Zhang, C., Guan, H., Zhang, Y. (2022). Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction. In: Guan, W. (eds) Epigenome-Wide Association Studies. Methods in Molecular Biology, vol 2432. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1994-0_15

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  • DOI: https://doi.org/10.1007/978-1-0716-1994-0_15

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1993-3

  • Online ISBN: 978-1-0716-1994-0

  • eBook Packages: Springer Protocols

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