ArticleGenomics, proteomics & bioinformatics2020
Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma.
Article in Genomics, proteomics & bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Review
- A clinical trial termination prediction model based on denoising autoencoder and deep survival regression.Quantitative biology (Beijing, China) · 2024Article
- tRNA-derived small RNAs in human cancers: roles, mechanisms, and clinical application.Molecular cancer · 2024Review
- Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.Scientific reports · 2024Article
- CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma.Frontiers in oncology · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.BMC infectious diseases · 2022Article
- Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network.Frontiers in computational neuroscience · 2022Article
- A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.International journal of molecular sciences · 2021Review
- Origins and evolving functionalities of tRNA-derived small RNAs.Trends in biochemical sciences · 2021Review
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- Article
- Autoencoder-Transformed Transcriptome Improves Genotype-Phenotype Association Studies.IEEE transactions on computational biology and bioinformaticsArticle
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8 authors.
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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.
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