ArticleBioMed research international2020
Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications.BMC medical informatics and decision making · 2025Pooled it
- Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.La Radiologia medica · 2025Pooled it
- Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.Frontiers in endocrinology · 2023Pooled it
- Harnessing Microbiome, Bacterial Extracellular Vesicle, and Artificial Intelligence for Polycystic Ovary Syndrome Diagnosis and Management.Biomolecules · 2025Review
- Exploring the predictive values of SERP4 and FRZB in dilated cardiomyopathy based on an integrated analysis.BMC cardiovascular disorders · 2024Article
- SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models.Diagnostics (Basel, Switzerland) · 2024Article
- Identification of fatty acid metabolism signature genes in patients with pulmonary arterial hypertension using WGCNA and machine learning.The Journal of international medical research · 2024Article
- Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis.BMC complementary medicine and therapies · 2023Article
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- Article
- A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network.Frontiers in genetics · 2023Article
- Contribution of labor related gene subtype classification on heterogeneity of polycystic ovary syndrome.PloS one · 2023Article
- Multi-omics and machine learning for the prevention and management of female reproductive health.Frontiers in endocrinology · 2023Review
- Establishment and Analysis of an Artificial Neural Network Model for Early Detection of Polycystic Ovary Syndrome Using Machine Learning Techniques.Journal of inflammation research · 2023Article
- Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome.Journal of ovarian research · 2022Article
- Identification and immune characteristics of molecular subtypes related to fatty acid metabolism in idiopathic pulmonary fibrosis.Frontiers in nutrition · 2022Article
- Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks.Frontiers in cardiovascular medicine · 2022Article
- Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network.Frontiers in aging neuroscience · 2022Article
- A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis.Frontiers in genetics · 2022Article
- Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.
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