Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network

Xie, Ningning; Wang, Fangfang; Zhou, Jue; Liu, Chang; Qu, Fan

doi:10.1155/2020/2613091

Cited by 40 publications

(24 citation statements)

References 51 publications

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“…The combination of random forest and artificial neural network can be used to construct a reliable predictive model for the diagnosis of some diseases, such as polycystic ovary syndrome (PCOS) ( Xie et al, 2020 ) and ulcerative colitis ( Li et al, 2020 ). In this study, we identified 2,552 DEGs associated with EMs in the GSE51981 dataset.…”

Section: Discussionmentioning

confidence: 99%

A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

She

Diao

et al. 2022

Front. Genet.

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Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs.

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Section: Discussionmentioning

confidence: 99%

A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

She

Diao

et al. 2022

Front. Genet.

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“…A prediction model based on an RF algorithm has been previously applied to several fields of clinical medicine. In particular, it has been used for the prediction of cardiovascular disease[ 8 ], the clinical outcome after aneurysm rupture at the time of discharge[ 9 ], diagnosis of polycystic ovary syndrome[ 10 ], and the effect of chemotherapy on patient tumors[ 11 ]. However, very few studies have reported the use of RF for the prediction of PIH in patients undergoing cardiac surgery.…”

Section: Introductionmentioning

confidence: 99%

Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery

Li¹,

Huang²,

Tang³

et al. 2021

WJCC

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BACKGROUND Hypotension after the induction of anesthesia is known to be associated with various adverse events. The involvement of a series of factors makes the prediction of hypotension during anesthesia quite challenging. AIM To explore the ability and effectiveness of a random forest (RF) model in the prediction of post-induction hypotension (PIH) in patients undergoing cardiac surgery. METHODS Patient information was obtained from the electronic health records of the Second Affiliated Hospital of Hainan Medical University. The study included patients, ≥ 18 years of age, who underwent cardiac surgery from December 2007 to January 2018. An RF algorithm, which is a supervised machine learning technique, was employed to predict PIH. Model performance was assessed by the area under the curve (AUC) of the receiver operating characteristic. Mean decrease in the Gini index was used to rank various features based on their importance. RESULTS Of the 3030 patients included in the study, 1578 (52.1%) experienced hypotension after the induction of anesthesia. The RF model performed effectively, with an AUC of 0.843 (0.808-0.877) and identified mean blood pressure as the most important predictor of PIH after anesthesia. Age and body mass index also had a significant impact. CONCLUSION The generated RF model had high discrimination ability for the identification of individuals at high risk for a hypotensive event during cardiac surgery. The study results highlighted that machine learning tools confer unique advantages for the prediction of adverse post-anesthesia events.

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“…AUC, accuracy, F1 score, precision, and recall were compared using four machine learning classifiers. Because all four machine learning classifiers have been used in many gene expression studies [93][94][95][96], it is difficult to know which classifier is suitable. Therefore, we used all four widely used machine learning classifiers.…”

Section: Discussionmentioning

confidence: 99%

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Haam

Han

Lee

et al. 2021

Cancers

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Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

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Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network

Cited by 40 publications

References 51 publications

A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

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