Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma

Jun Wang, Xueying Xie, Junchao Shi, Wenjun He, Qi Chen, Liang Chen, Wanjun Gu, Tong Zhou

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

Original languageEnglish (US)
Pages (from-to)468-480
Number of pages13
JournalGenomics, Proteomics and Bioinformatics
Volume18
Issue number4
DOIs
StatePublished - Aug 2020
Externally publishedYes

Keywords

  • Denoising autoencoder
  • Lung cancer
  • Molecular signature
  • Prognosis
  • Unsupervised learning

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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