Literature DB >> 35505217

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

Lechuan Li1, Chonghao Zhang1, Hannah Guan1, Yu Zhang2.   

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.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Aging; DNA methylation; Machine learning; Neural networks; Prediction

Mesh：

Year:  2022        PMID： 35505217     DOI： 10.1007/978-1-0716-1994-0_15

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN： 1064-3745


  1 in total

1.  Utilizing Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women.

Authors:  Paul Fergus; Casimiro Curbelo Montanez; Basma Abdulaimma; Paulo Lisboa; Carl Chalmers; Beth Pineles
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2018-09-03       Impact factor: 3.710
  1 in total

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