ArticleBMC bioinformatics2023
Hybrid deep learning approach to improve classification of low-volume high-dimensional data.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- Artificial intelligence and predictive tools in non-muscle invasive bladder cancer: a narrative review of current insights and advances.Translational andrology and urology · 2026Review
- Interpretable Skin Cancer Identification Using a Hybrid Deep Learning and XAI Framework on HAM10000.Bioengineering (Basel, Switzerland) · 2026Article
- Explainable hybrid machine learning model for predicting stunting and identifying key risk factors among Ethiopian children under five.Scientific reports · 2026Article
- Advanced deep learning techniques for classifying dental conditions using panoramic X-ray images.BMC oral health · 2026Article
- Enhancing deep learning interpretability for hand-crafted feature-guided histologic image classification via weak-to-strong generalization.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Article
- Automated meningioma detection using skull X ray images with deep learning and machine learning classifiers.Scientific reports · 2025Article
- Multi-label diagnosis of dental conditions from panoramic x-rays using attention-enhanced deep learning.Oral and maxillofacial surgery · 2025Article
- Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.BMC public health · 2025Article
- A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers.BMC bioinformatics · 2025Article
- Deep-learning-ready RGB-depth images of seedling development.Plant methods · 2025Article
- Deep learning based deconvolution methods: A systematic review.Computational and structural biotechnology journal · 2025Review
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- Breaking new ground: machine learning enhances survival forecasts in hypercapnic respiratory failure.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well in high-dimensional datasets. Deep learning has shown success in feature generation but requires large datasets to achieve high classification accuracy. Biology domains typically exhibit these challenges with numerous handcrafted features (high-dimensional) and small amounts of training data (low volume).
methodA hybrid learning approach is proposed that first trains a deep network on the training data, extracts features from the deep network, and then uses these features to re-express the data for input to a non-deep learning method, which is trained to perform the final classification.
resultsThe approach is systematically evaluated to determine the best layer of the deep learning network from which to extract features and the threshold on training data volume that prefers this approach. Results from several domains show that this hybrid approach outperforms standalone deep and non-deep learning methods, especially on low-volume, high-dimensional datasets. The diverse collection of datasets further supports the robustness of the approach across different domains.
conclusionsThe hybrid approach combines the strengths of deep and non-deep learning paradigms to achieve high performance on high-dimensional, low volume learning tasks that are typical in biology domains.
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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.