Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

Sun, Daoyuan; Peng, Haojun; Wu, Zhibing

doi:10.3389/fnagi.2022.921906

Cited by 17 publications

(14 citation statements)

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“…Alzheimer's disease. 53 Besides, the high AUC score of ROC and the results of DCA in our predictive model showed that the classifier for periodontitis achieved excellent accuracy in discriminating periodontitis from healthy controls with a promising probability in microarray data.…”

Section: Discussionmentioning

confidence: 72%

“…The outstanding novelty of our study was the first employed an ANN model to construct a classifier based on the lncRNAs in three necroptosis‐related regulatory axes and combining experimental validation, which yielded excellent results in terms of predictive power. This novel research approach has been beneficial for several other diseases, including polycystic ovary syndrome 52 and Alzheimer's disease 53 . Besides, the high AUC score of ROC and the results of DCA in our predictive model showed that the classifier for periodontitis achieved excellent accuracy in discriminating periodontitis from healthy controls with a promising probability in microarray data.…”

Section: Discussionmentioning

confidence: 73%

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Revealing a potential necroptosis‐related axis (RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1) in periodontitis by construction of the ceRNA network

Wang

Chen

Peng

et al. 2023

J of Periodontal Research

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Background and ObjectivesPeriodontitis, a prevalent chronic inflammatory condition, poses a significant risk of tooth loosening and subsequent tooth loss. Within the realm of programmed cell death, a recently recognized process known as necroptosis has garnered attention for its involvement in numerous inflammatory diseases. Nevertheless, its correlation with periodontitis is indistinct. Our study aimed to identify necroptosis‐related lncRNAs and crucial lncRNA‐miRNA‐mRNA regulatory axes in periodontitis to further understand the pathogenesis of periodontitis.Materials and MethodsGene expression profiles in gingival tissues were acquired from the Gene Expression Omnibus (GEO) database. Selecting hub necroptosis‐related lncRNA and extracting the key lncRNA‐miRNA‐mRNA axes based on the ceRNA network by adding novel machine‐learning models based on conventional analysis and combining qRT‐PCR validation. Then, an artificial neural network (ANN) model was constructed for lncRNA in regulatory axes, and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Weighted correlation network analysis (WGCNA) and single‐sample gene set enrichment analysis (ssGSEA) was performed to explore how these lncRNAs work in periodontitis.ResultsSeven hub necroptosis‐related lncRNAs and three lncRNA‐miRNA‐mRNA regulatory axes (RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1 axis, RP11‐96D1.11/hsa‐miR‐185‐5p/EZH2 axis, and RP4‐773 N10.4/hsa‐miR‐21‐5p/TLR3 axis) were predicted. WGCNA revealed that RP11‐138A9.1 was significantly correlated with the “purple module”. Functional enrichment analysis and ssGSEA demonstrated that the RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1 axis is closely related to the inflammation and immune processes in periodontitis.ConclusionOur study predicted a crucial necroptosis‐related regulatory axis (RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1) based on the ceRNA network, which may aid in elucidating the role and mechanism of necroptosis in periodontitis.

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Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 73%

Revealing a potential necroptosis‐related axis (RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1) in periodontitis by construction of the ceRNA network

Wang

Chen

Peng

et al. 2023

J of Periodontal Research

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“…The key bene ts of Random Forest (RF) are its accuracy and resistance to over tting, which makes it a good choices of machine learning algorithms (12). The use of Arti cial Neural Networks (ANNs) allows for the creation of nonlinear models, and make it is possible to detect nonlinear relationships and all potential interactions among predictor variables (13).It has been reported that RF and ANN can be used in conjunction to make e cient diagnoses in a wide range of diseases, such as Alzheimer"s disease, heart failure and periodontitis (12,14,15).…”

Section: Introductionmentioning

confidence: 99%

Construction of Predictive Model of Interstitial Fibrosis and Tubular Atrophy (IFTA) After Kidney Transplantation with Machine Learning Algorithm

Yao

Chen

Zhang

et al. 2023

Preprint

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Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of CKD and one of the causes of long-term renal loss in transplanted kidneys. The purpose of our study is to screen IFTA-related genes with higher importance scores through Random Forest (RF) and further construct IFTA diagnostic model through Artificial Neural Networks (ANNs). Methods: We screened all 162 “kidney transplant” related cohorts in the GEO database and obtained 5 data sets (training sets: GSE98320 validation sets: GSE22459, GSE53605 and GSE76882 survival sets: GSE21374). Differentially expressed genes (DEGs) analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Random Forest (RF), Artificial Neural Network (ANN), Unsupervised Clustering analysis, CIBERSORT analysis were used to analyze the data. Results: A total of 108 common DEGs were identified by taking the intersection of the DEGs of our training sets and validation sets. A total of 15 top IFTA-specific DEGs were screened through the RF, then was used to build ANNs models. The model has good performance in both the training sets [GSE98320 (AUC = 0.9560)] and the validation sets [GSE22459 (AUC = 0.720), GSE53605 (AUC =0.938), GSE76882 (AUC = 0.781)], indicating that we have avoided overfitting while improving the accuracy. Furthermore, samples of survival sets are divided into two clusters using consensus clustering algorithm basing on the expression of 15 top IFTA-specific DEGs. We found significant differences between the two subgroups by survival analysis, and further enrichment analysis and immune cell infiltration analysis were conducted to further explore the causes of survival differences. Conclusion: we identified key biomarkers of IFTA and developed a new IFTA classification model, basing on the combination of RF and ANNs.

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“…However, feature selection remains a major bottleneck in building multi-gene classification models. This concern is well addresses by the application of various machine learning techniques in biology nowadays (10)(11)(12). These algorithms, when used individually or in combination, have made significant contributions to the classification of gene expression data, disease detection, and microbiome studies (13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

Establishment and analysis of a disease risk prediction model for the systemic lupus erythematosus with random forest

et al. 2022

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Systemic lupus erythematosus (SLE) is a latent, insidious autoimmune disease, and with the development of gene sequencing in recent years, our study aims to develop a gene-based predictive model to explore the identification of SLE at the genetic level. First, gene expression datasets of SLE whole blood samples were collected from the Gene Expression Omnibus (GEO) database. After the datasets were merged, they were divided into training and validation datasets in the ratio of 7:3, where the SLE samples and healthy samples of the training dataset were 334 and 71, respectively, and the SLE samples and healthy samples of the validation dataset were 143 and 30, respectively. The training dataset was used to build the disease risk prediction model, and the validation dataset was used to verify the model identification ability. We first analyzed differentially expressed genes (DEGs) and then used Lasso and random forest (RF) to screen out six key genes (OAS3, USP18, RTP4, SPATS2L, IFI27 and OAS1), which are essential to distinguish SLE from healthy samples. With six key genes incorporated and five iterations of 10-fold cross-validation performed into the RF model, we finally determined the RF model with optimal mtry. The mean values of area under the curve (AUC) and accuracy of the models were over 0.95. The validation dataset was then used to evaluate the AUC performance and our model had an AUC of 0.948. An external validation dataset (GSE99967) with an AUC of 0.810, an accuracy of 0.836, and a sensitivity of 0.921 was used to assess the model’s performance. The external validation dataset (GSE185047) of all SLE patients yielded an SLE sensitivity of up to 0.954. The final high-throughput RF model had a mean value of AUC over 0.9, again showing good results. In conclusion, we identified key genetic biomarkers and successfully developed a novel disease risk prediction model for SLE that can be used as a new SLE disease risk prediction aid and contribute to the identification of SLE.

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Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network

Cited by 17 publications

References 50 publications

Revealing a potential necroptosis‐related axis (RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1) in periodontitis by construction of the ceRNA network

Revealing a potential necroptosis‐related axis (RP11‐138A9.1/hsa‐miR‐98‐5p/ZBP1) in periodontitis by construction of the ceRNA network

Construction of Predictive Model of Interstitial Fibrosis and Tubular Atrophy (IFTA) After Kidney Transplantation with Machine Learning Algorithm

Establishment and analysis of a disease risk prediction model for the systemic lupus erythematosus with random forest

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