scholarly journals Random Forest Model For Predicting IDH1 Gene Expression Based on Volumetric Texture Parameters of WHO Grade II/III Glioma and Peritumoral EdemaRandom Forest Model for Predicting IDH1 Gene Expression Based on Volumetric Texture Parameters of WHO Grade II/III Glioma and Peritumoral Edema

2021 ◽  
Author(s):  
Wenting Lan ◽  
Zhan Feng ◽  
Yan Zhang ◽  
ZhengYa Zhao ◽  
Yi Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Random Forest ◽  
Random Forest Model ◽  
Peritumoral Edema ◽  
Wild Type ◽  
Who Grade ◽  
Forest Model ◽  
Texture Parameters ◽  
Grade Ii ◽  
Volumetric Texture

Abstract Background: The incidence of Isocitrate dehydrogenase (IDH) gene mutation had closed contact with the development and prognosis of WHO grade II/III glioma. This study aims to establish and evaluate the predicting random Forest model for IDH1 gene mutation based on parenchyma and peritumoral edema ADC image texture parameters of WHO grade II/III glioma. Materials and Methods: 146 patients (77 males and 69 females) with histologically confirmed anaplastic glioma were divided into training and validation groups in a ratio of 7:3 according to the requirements of Random Forest Model. The training group consisted of 102 patients (42 IDH1 mutant and 60 wild type) and the validation group included 44 patients (18 IDH1 mutant and 26 wild type). Conventional MRI features of two independent samples (IDH1 mutant and wild type) were evaluated by the Visually Accessible Rembrandt Images (VASARI) scoring system, Texture analysis (TA) of ADC image was based on the entire tumor volume and peritumoral edema and was used as Principal component analysis (PCA) to screen texture features labels. Random forest diagnosis models (VASARI+TumorADC、VASARI+TumorADC+EdemaADC) were constructed on the basis of morphological single-factor variables, texture feature labels. Result: The diagnostic accuracy of the random forest diagnosis model (VASARI+Tumor ADC) was 71.5%, the specificity was 75.40%, and the AUC was 0.769, The model (VASARI+TumorADC +peritumoral edema ADC ) was 80.9%, 79.5% ,and 0.819 correspondingly. Conclusion: The texture parameters of peritumoral edema ADC image were non-invasive markers to predict IDH1 mutational status and they have played a certain role in improving the efficiency of diagnostic model.

scholarly journals IDH mutation status is associated with distinct vascular gene expression signatures in lower-grade gliomas

2018 ◽  
Vol 20 (11) ◽  
pp. 1505-1516 ◽  
Author(s):  
Lei Zhang ◽  
Liqun He ◽  
Roberta Lugano ◽  
Kenney Roodakker ◽  
Michael Bergqvist ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Lower Grade ◽  
Idh Mutation ◽  
Wild Type ◽  
Who Grade ◽  
Mutation Status ◽  
Grade Ii ◽  
Grade Ii Glioma

Abstract Background Vascular gene expression patterns in lower-grade gliomas (LGGs; diffuse World Health Organization [WHO] grades II–III gliomas) have not been thoroughly investigated. The aim of this study was to molecularly characterize LGG vessels and determine if tumor isocitrate dehydrogenase (IDH) mutation status affects vascular phenotype. Methods Gene expression was analyzed using an in-house dataset derived from microdissected vessels and total tumor samples from human glioma in combination with expression data from 289 LGG samples available in the database of The Cancer Genome Atlas. Vascular protein expression was examined by immunohistochemistry in human brain tumor tissue microarrays (TMAs) representing WHO grades II–IV gliomas and nonmalignant brain samples. Regulation of gene expression was examined in primary endothelial cells in vitro. Results Gene expression analysis of WHO grade II glioma indicated an intermediate stage of vascular abnormality, less severe than that of glioblastoma vessels but distinct from normal vessels. Enhanced expression of laminin subunit alpha 4 (LAMA4) and angiopoietin 2 (ANGPT2) in WHO grade II glioma was confirmed by staining of human TMAs. IDH wild-type LGGs displayed a specific angiogenic gene expression signature, including upregulation of ANGPT2 and serpin family H (SERPINH1), connected to enhanced endothelial cell migration and matrix remodeling. Transcription factor analysis indicated increased transforming growth factor beta (TGFβ) and hypoxia signaling in IDH wild-type LGGs. A subset of genes specifically induced in IDH wild-type LGG vessels was upregulated by stimulation of endothelial cells with TGFβ2, vascular endothelial growth factor, or cobalt chloride in vitro. Conclusion IDH wild-type LGG vessels are molecularly distinct from the vasculature of IDH-mutated LGGs. TGFβ and hypoxia-related signaling pathways may be potential targets for anti-angiogenic therapy of IDH wild-type LGG.

Identification of the diagnostic signature of sepsis based on bioinformatic analysis of gene expression and machine learning

2020 ◽  
Vol 23 ◽  
Author(s):  
Qian Zhao ◽  
Ning Xu ◽  
Hui Guo ◽  
Jianguo Li
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Random Forest ◽  
Early Diagnosis ◽  
Bioinformatic Analysis ◽  
Random Forest Model ◽  
Diagnostic Methods ◽  
Ppi Network ◽  
Hub Genes ◽  
Forest Model

Background: Sepsis is a life-threatening disease caused by the dysregulated host response to the infection, and being the major cause of death to patients in intensive care unit (ICU). Objective: Early diagnosis of sepsis could significantly reduce in-hospital mortality. Though generated from infection, the development of sepsis follows its own psychological process and disciplines, alters with gender, health status and other factors. Hence, the analysis of mass data by bioinformatic tools and machine learning is a promising method for exploring early diagnosis manners. Method: We collected miRNA and mRNA expression data of sepsis blood samples from Gene Expression Omnibus (GEO) and ArrayExpress databases, screened out differentially expressed genes (DEGs) by R software, predicted miRNA targets on TargetScanHuman and miRTarBase websites, conducted Gene Ontology (GO) term and KEGG pathway enrichment based on overlapping DEGs. The STRING database and Cytoscape were used to build protein-protein interaction (PPI) network and predict hub genes. Then we constructed a Random Forest model by using the hub genes to assess sample type. Results: Bioinformatic analysis of GEO dataset revealed 46 overlapping DEGs in sepsis. The PPI network analysis identified five hub genes, SOCS3, KBTBD6, FBXL5, FEM1C and WSB1. Random Forest model based on these five hub genes was used to assess GSE95233 and GSE95233 datasets, and the area under curve (AUC) of ROC are 0.900 and 0.7988, respectively, which confirmed the efficacy of this model. Conclusion: The integrated analysis of gene expression in sepsis and the effective Random Forest model built in this study may provide promising diagnostic methods for sepsis.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

scholarly journals Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

2021 ◽  
pp. 100017
Author(s):  
Xinyu Dou ◽  
Cuijuan Liao ◽  
Hengqi Wang ◽  
Ying Huang ◽  
Ying Tu ◽  
...  
Keyword(s):  
Random Forest ◽  
Ground Level ◽  
Random Forest Model ◽  
Forest Model

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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