Establishment of a Combined Diagnostic Model of Abdominal Aortic Aneurysm with Random Forest and Artificial Neural Network

Duan, Yixuan; Xie, Enrui; Liu, Chang; Sun, Jingjing; Deng, Jie

doi:10.1155/2022/7173972

Cited by 10 publications

(8 citation statements)

References 38 publications

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“…As detailed above, DEFA1 and SFRP2 may indicate a role for dysregulated inflammatory processes and Wnt signaling in IA, respectively. HBA1 and HBA2 both encode alpha subunits of hemoglobin and have also been found to be increased in abdominal aortic aneurysms [ 41 , 42 ]. Aside from its oxygen carrying function, hemoglobin has been found to be a regulator of iron metabolism and an antioxidant in different tissue types [ 43 , 44 ], which may play a role in the inflammation associated with IA.…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, the DEGs identified here may overlap with those identified in other vascular diseases, which could limit the specificity of the biomarker. For example, several (5/39) of the significant genes (including LPL [ 46 ], IFI44L [ 47 ], ACKR2 [ 47 ], HBA1 [ 41 ], and HBA2 [ 42 ]) have also been found to be differently expressed in abdominal aortic aneurysms.…”

Section: Discussionmentioning

confidence: 99%

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RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood

Poppenberg

Chien

Santo

et al. 2023

JPM

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After detection, identifying which intracranial aneurysms (IAs) will rupture is imperative. We hypothesized that RNA expression in circulating blood reflects IA growth rate as a surrogate of instability and rupture risk. To this end, we performed RNA sequencing on 66 blood samples from IA patients, for which we also calculated the predicted aneurysm trajectory (PAT), a metric quantifying an IA’s future growth rate. We dichotomized dataset using the median PAT score into IAs that were either more stable and more likely to grow quickly. The dataset was then randomly divided into training (n = 46) and testing cohorts (n = 20). In training, differentially expressed protein-coding genes were identified as those with expression (TPM > 0.5) in at least 50% of the samples, a q-value < 0.05 (based on modified F-statistics with Benjamini-Hochberg correction), and an absolute fold-change ≥ 1.5. Ingenuity Pathway Analysis was used to construct networks of gene associations and to perform ontology term enrichment analysis. The MATLAB Classification Learner was then employed to assess modeling capability of the differentially expressed genes, using a 5-fold cross validation in training. Finally, the model was applied to the withheld, independent testing cohort (n = 20) to assess its predictive ability. In all, we examined transcriptomes of 66 IA patients, of which 33 IAs were “growing” (PAT ≥ 4.6) and 33 were more “stable”. After dividing dataset into training and testing, we identified 39 genes in training as differentially expressed (11 with decreased expression in “growing” and 28 with increased expression). Model genes largely reflected organismal injury and abnormalities and cell to cell signaling and interaction. Preliminary modeling using a subspace discriminant ensemble model achieved a training AUC of 0.85 and a testing AUC of 0.86. In conclusion, transcriptomic expression in circulating blood indeed can distinguish “growing” and “stable” IA cases. The predictive model constructed from these differentially expressed genes could be used to assess IA stability and rupture potential.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood

Poppenberg

Chien

Santo

et al. 2023

JPM

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“…Therefore, objective and quantitative methods for the early diagnosis of periodontitis are urgently needed. Advances in machine learning have enabled the development Frontiers in Genetics frontiersin.org of using biomarkers for disease diagnosis and prognosis (Duan et al, 2022;Wu et al, 2022). Papantonopoulos et al first applied an ANN algorithm to develop a classification model for chronic and aggressive periodontitis based on immune parameters (Papantonopoulos et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, objective and quantitative methods for the early diagnosis of periodontitis are urgently needed. Advances in machine learning have enabled the development of using biomarkers for disease diagnosis and prognosis ( Duan et al, 2022 ; Wu et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis

Xiang

Huang

et al. 2022

Front. Genet.

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Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN).Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis.Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model’s accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function.Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.

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“…To date, employment of machine learning algorithms and wide application of gene expression data in public databases offer methods to find biomarkers for cancer diagnosis in a list of fields Tabl et al, 2019). The integration of RF and ANN can be employed to establish a stable diagnostic model for some diseases, like ulcerative colitis and abdominal aortic aneurysm (Li et al, 2020;Duan et al, 2022). Papillary thyroid carcinoma is characterized by slow progression and good prognosis.…”

Section: Discussionmentioning

confidence: 99%

Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest

Wang

Liu²,

Ye³

et al. 2022

Front. Genet.

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Objective: Papillary thyroid carcinoma (PTC) accounts for 80% of thyroid malignancy, and the occurrence of PTC is increasing rapidly. The present study was conducted with the purpose of identifying novel and important gene panels and developing an early diagnostic model for PTC by combining artificial neural network (ANN) and random forest (RF).Methods and results: Samples were searched from the Gene Expression Omnibus (GEO) database, and gene expression datasets (GSE27155, GSE60542, and GSE33630) were collected and processed. GSE27155 and GSE60542 were merged into the training set, and GSE33630 was defined as the validation set. Differentially expressed genes (DEGs) in the training set were obtained by “limma” of R software. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis as well as immune cell infiltration analysis were conducted based on DEGs. Important genes were identified from the DEGs by random forest. Finally, an artificial neural network was used to develop a diagnostic model. Also, the diagnostic model was validated by the validation set, and the area under the receiver operating characteristic curve (AUC) value was satisfactory.Conclusion: A diagnostic model was established by a joint of random forest and artificial neural network based on a novel gene panel. The AUC showed that the diagnostic model had significantly excellent performance.

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Establishment of a Combined Diagnostic Model of Abdominal Aortic Aneurysm with Random Forest and Artificial Neural Network

Cited by 10 publications

References 38 publications

RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood

RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood

Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis

Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest

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