Learning from small medical data—robust semi-supervised cancer prognosis classifier with Bayesian variational autoencoder

Abstract Motivation Cancer is one of the world’s leading mortality causes, and its prognosis is hard to predict due to complicated biological interactions among heterogeneous data types. Numerous challenges, such as censorship, high dimensionality and small sample size, prevent researchers from usin...

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Veröffentlicht in:Bioinformatics advances Jg. 3; H. 1; S. vbac100
Hauptverfasser: Hsu, Te-Cheng, Lin, Che
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 2023
Schlagworte:
Bayesian analysis
Bioinformatics
Breast cancer
Deep learning
Mathematical models
Medical prognosis
Neural networks
Non-small cell lung carcinoma
Original Paper
Precision medicine
Prognosis
Small cell lung carcinoma
ISSN:2635-0041, 2635-0041
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Zusammenfassung:Abstract Motivation Cancer is one of the world’s leading mortality causes, and its prognosis is hard to predict due to complicated biological interactions among heterogeneous data types. Numerous challenges, such as censorship, high dimensionality and small sample size, prevent researchers from using deep learning models for precise prediction. Results We propose a robust Semi-supervised Cancer prognosis classifier with bAyesian variational autoeNcoder (SCAN) as a structured machine-learning framework for cancer prognosis prediction. SCAN incorporates semi-supervised learning for predicting 5-year disease-specific survival and overall survival in breast and non-small cell lung cancer (NSCLC) patients, respectively. SCAN achieved significantly better AUROC scores than all existing benchmarks (81.73% for breast cancer; 80.46% for NSCLC), including our previously proposed bimodal neural network classifiers (77.71% for breast cancer; 78.67% for NSCLC). Independent validation results showed that SCAN still achieved better AUROC scores (74.74% for breast; 72.80% for NSCLC) than the bimodal neural network classifiers (64.13% for breast; 67.07% for NSCLC). SCAN is general and can potentially be trained on more patient data. This paves the foundation for personalized medicine for early cancer risk screening. Availability and implementation The source codes reproducing the main results are available on GitHub: https://gitfront.io/r/user-4316673/36e8714573f3fbfa0b24690af5d1a9d5ca159cf4/scan/. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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ISSN:2635-0041
2635-0041
DOI:10.1093/bioadv/vbac100