EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models Article Swipe

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Xiangju Liu , Yu Zhang , Chunli Fu , Ruochi Zhang , Fengfeng Zhou ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.3389/fgene.2021.636429

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.

Related Topics

Feature Selection

Artificial Intelligence

Computer Science

Ensemble Learning

Pulmonary Hypertension

Disease

Support Vector Machine

Machine Learning

Computational Biology

Concepts

Feature selection Lasso (programming language) Artificial intelligence Feature (linguistics) Ranking (information retrieval) Computer science Selection (genetic algorithm) Ensemble learning Pulmonary hypertension Pattern recognition (psychology) Transcriptome Disease Support vector machine Machine learning Computational biology Medicine Biology Internal medicine Gene expression Gene Biochemistry Philosophy World Wide Web Linguistics

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Type: article
Language: en
Landing Page: https://doi.org/10.3389/fgene.2021.636429
OA Status: gold
Cited By: 12
References: 56
Related Works: 10
OpenAlex ID: https://openalex.org/W3157984229

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DOI: https://doi.org/10.3389/fgene.2021.636429

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Title: EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models

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Language: en

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Publication year: 2021

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Publication date: 2021-04-27

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Authors: Xiangju Liu, Yu Zhang, Chunli Fu, Ruochi Zhang, Fengfeng Zhou

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Landing page: https://doi.org/10.3389/fgene.2021.636429

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Concepts: Feature selection, Lasso (programming language), Artificial intelligence, Feature (linguistics), Ranking (information retrieval), Computer science, Selection (genetic algorithm), Ensemble learning, Pulmonary hypertension, Pattern recognition (psychology), Transcriptome, Disease, Support vector machine, Machine learning, Computational biology, Medicine, Biology, Internal medicine, Gene expression, Gene, Biochemistry, Philosophy, World Wide Web, Linguistics

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Cited by: 12

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