ArticleFrontiers in genetics2021
EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 12 citations in OpenAlex.
- Predicting survival factor following suicide attempt in Iran: an ensemble machine learning technique.BMC psychiatry · 2025Article
- Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques.Health science reports · 2025Article
- Article
- Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension.Journal of clinical hypertension (Greenwich, Conn.) · 2023Article
- Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- Hypertension-Related Drug Activity Identification Based on Novel Ensemble Method.Frontiers in genetics · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
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Abstract
Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an ideal source for a minimally invasive disease diagnosis. This study hypothesized that the transcriptional fluctuations in the PMBCs exposed to the PH arteries may stably reflect the disease. However, the dimension of a human transcriptome is much higher than the number of samples in all the existing datasets. So, an ensemble feature selection algorithm, EnRank, was proposed to integrate the ranking information of four popular feature selection algorithms, i.e., T-test (Ttest), Chi-squared test (Chi2), ridge regression (Ridge), and Least Absolute Shrinkage and Selection Operator (Lasso). Our results suggested that the EnRank-detected biomarkers provided useful information from these four feature selection algorithms and achieved very good prediction accuracy in predicting the PH patients. Many of the EnRank-detected biomarkers were also supported by the literature.
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