ArticleGenome biology2024
SyntheVAEiser: augmenting traditional machine learning methods with VAE-based gene expression sample generation for improved cancer subtype predictions.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Eleven quick tips for Biomedical Federated Learning.PLoS computational biology · 2026Article
- Leveraging population-scale proteomic data with deep learning for head and neck cancer detection in saliva.NPJ digital medicine · 2026Article
- Semisupervised approach for dominant gene selection and classification.BMC bioinformatics · 2026Article
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- MetaPaCS: A Novel Meta-Learning Framework for Pancreatic Cancer Subtype Identification.bioRxiv : the preprint server for biology · 2026Article
- Primer on Modelling Approaches for Omics Data.Advances in experimental medicine and biology · 2026Article
- STRIVE: interpretable multimodal subtype discovery in Women's cancers using an attention-weighted joint variational autoencoder.Frontiers in bioinformatics · 2026Article
- Development and validation of an early predictive model for hemiplegic shoulder pain: a comparative study of logistic regression, support vector machine, and random forest.Frontiers in neurology · 2025Article
- SyntheVAEiser: augmenting traditional machine learning methods with VAE-based gene expression sample generation for improved cancer subtype predictions.Genome biology · 2024Article
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Authors and funding
7 authors.
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Abstract
The accuracy of machine learning methods is often limited by the amount of training data that is available. We proposed to improve machine learning training regimes by augmenting datasets with synthetically generated samples. We present a method for synthesizing gene expression samples and test the system's capabilities for improving the accuracy of categorical prediction of cancer subtypes. We developed SyntheVAEiser, a variational autoencoder based tool that was trained and tested on over 8000 cancer samples. We have shown that this technique can be used to augment machine learning tasks and increase performance of recognition of underrepresented cohorts.
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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.