ArticleFEBS open bio2019
Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders.
Article in FEBS open bio, 2019. 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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Who cites it
14 citing papers in PubMed.
- Implementing physics-informed neural networks with deep learning for differential equations.Frontiers in artificial intelligence · 2026Article
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- Machine learning in the differential diagnosis of ulcerative colitis and Crohn's disease: a systematic review.Translational gastroenterology and hepatology · 2025Review
- Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Machine Learning to Identify Critical Biomarker Profiles in New SARS-CoV-2 Variants.Microorganisms · 2024Article
- Artificial intelligence in inflammatory bowel disease: implications for clinical practice and future directions.Intestinal research · 2023Review
- Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach.Medicina (Kaunas, Lithuania) · 2023Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- Impact of artificial intelligence on prognosis, shared decision-making, and precision medicine for patients with inflammatory bowel disease: a perspective and expert opinion.Annals of medicine · 2023Review
- Artificial intelligence and inflammatory bowel disease: practicalities and future prospects.Frontline gastroenterology · 2022Article
- Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions.World journal of gastroenterology · 2021Review
- Strategies for Optimizing the Production of Proteins and Peptides with Multiple Disulfide Bonds.Antibiotics (Basel, Switzerland) · 2020Review
- An Introductory Review of Deep Learning for Prediction Models With Big Data.Frontiers in artificial intelligence · 2020Review
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Genomics data provide great opportunities for translational research and the clinical practice, for example, for predicting disease stages. However, the classification of such data is a challenging task due to their high dimensionality, noise, and heterogeneity. In recent years, deep learning classifiers generated much interest, but due to their complexity, so far, little is known about the utility of this method for genomics. In this paper, we address this problem by studying a computational diagnostics task by classification of breast cancer and inflammatory bowel disease patients based on high-dimensional gene expression data. We provide a comprehensive analysis of the classification performance of deep belief networks (DBNs) in dependence on its multiple model parameters and in comparison with support vector machines (SVMs). Furthermore, we investigate combined classifiers that integrate DBNs with SVMs. Such a classifier utilizes a DBN as representation learner forming the input for a SVM. Overall, our results provide guidelines for the complex usage of DBN for classifying gene expression data from complex diseases.
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