ArticleBMC medical informatics and decision making2024
A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Leveraging population-scale proteomic data with deep learning for head and neck cancer detection in saliva.NPJ digital medicine · 2026Article
- T-SMmOTE: tweaked synthetic majority minority oversampling technique for data scarcity issue in multi omics studies.Bioinformatics advances · 2026Article
- Primer on Modelling Approaches for Omics Data.Advances in experimental medicine and biology · 2026Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Synthetic data generation methods in healthcare: A review on open-source tools and methods.Computational and structural biotechnology journal · 2024Review
- Deep learning assisted cancer disease prediction from gene expression data using WT-GAN.BMC medical informatics and decision making · 2024Article
- PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.International journal of molecular sciences · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.