ArticleProceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology2017
An Ensemble Feature Selection Method for Biomarker Discovery.
Article in Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Machine learning-based screening of potential diagnostic markers for prostate cancer with immune cell infiltration analysis and experimental validation.Translational andrology and urology · 2025Article
- Double-Weighted Bayesian Model Combination for Metabolomics Data Description and Prediction.Metabolites · 2025Article
- miRDM-rfGA: Genetic algorithm-based identification of a miRNA set for detecting type 2 diabetes.BMC medical genomics · 2023Article
- Skin-Interfaced Wearable Sweat Sensors for Precision Medicine.Chemical reviews · 2023Review
- Precision Medicine Approaches with Metabolomics and Artificial Intelligence.International journal of molecular sciences · 2022Review
- Exploring Factors That Affected Student Well-Being during the COVID-19 Pandemic: A Comparison of Data-Mining Approaches.International journal of environmental research and public health · 2022Article
- A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data.Diagnostics (Basel, Switzerland) · 2022Article
- Novel feature selection methods for construction of accurate epigenetic clocks.PLoS computational biology · 2022Article
- Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data.Metabolomics : Official journal of the Metabolomic Society · 2022Article
- Supervised Methods for Biomarker Detection from Microarray Experiments.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI).Sensors (Basel, Switzerland) · 2021Article
- Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Rapid Solution-Phase Hydrogen/Deuterium Exchange for Metabolite Compound Identification.Journal of the American Society for Mass Spectrometry · 2019Article
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Authors and funding
6 authors.
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Abstract
Feature selection in Liquid Chromatography-Mass Spectrometry (LC-MS)-based metabolomics data (biomarker discovery) have become an important topic for machine learning researchers. High dimensionality and small sample size of LC-MS data make feature selection a challenging task. The goal of biomarker discovery is to select the few most discriminative features among a large number of irreverent ones. To improve the reliability of the discovered biomarkers, we use an ensemble-based approach. Ensemble learning can improve the accuracy of feature selection by combining multiple algorithms that have complementary information. In this paper, we propose an ensemble approach to combine the results of filter-based feature selection methods. To evaluate the proposed approach, we compared it to two commonly used methods, t-test and PLS-DA, using a real data set.
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Registered trials
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