ArticleComputational and structural biotechnology journal2020
Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression.
Article in Computational and structural biotechnology journal, 2020. 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.
- Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China.BMC pregnancy and childbirth · 2024Article
- LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.Bioinformatics (Oxford, England) · 2023Article
- Classification and prediction of spinal disease based on the SMOTE-RFE-XGBoost model.PeerJ. Computer science · 2023Article
- Article
- A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study.Frontiers in cardiovascular medicine · 2022Article
- Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models.Journal of translational medicine · 2021Article
- Reply to: "Inconsistent prediction capability of ImmuneCells.Sig across different RNA-seq datasets".Nature communications · 2021Article
- Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China.Clinical epidemiology · 2021Article
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2 authors.
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Abstract
In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. In recent years, various approaches have been proposed for the feature selection of identifying the subset of meaningful genes that can achieve accurate classification for disease samples from controls. Here, we comprehensively summarize the regularized logistic regression with seven effective penalties developed for the selection of strongly indicative genes of SPTB from microarray data. We compare their properties and assess their classification performances in multiple datasets. It shows that elastic net, lasso,
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