scholarly journals Influence of Soil Type on the Reliability of the Prediction Model for Bioavailability of Mn, Zn, Pb, Ni and Cu in the Soils of the Republic of Serbia

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 141
Author(s):  
Jelena Maksimović ◽  
Radmila Pivić ◽  
Aleksandra Stanojković-Sebić ◽  
Marina Jovković ◽  
Darko Jaramaz ◽  
...  
Keyword(s):  
Soil Type ◽  
Information Technologies ◽  
Multidisciplinary Approach ◽  
Prediction Models ◽  
Model Development ◽  
Training Set ◽  
Reliable Prediction ◽  
The Republic ◽  
Adequate Analysis ◽  
Testing Set

The principles of sustainable agriculture in the 21st century are based on the preservation of basic natural resources and environmental protection, which is achieved through a multidisciplinary approach in obtaining solutions and applying information technologies. Prediction models of bioavailability of trace elements (TEs) represent the basis for the development of machine learning and artificial intelligence in digital agriculture. Since the bioavailability of TEs is influenced by the physicochemical properties of the soil, which are characteristic of the soil type, in order to obtain more reliable prediction models in this study, the testing set from the previous study was grouped based on the soil type. The aim of this study was to examine the possibility of improvement in the prediction of bioavailability of TEs by using a different strategy of model development. After the training set was grouped based on the criteria for the new model development, the developed basic models were compared to the basic models from the previous study. The second step was to develop models based on the soil type (for the eight most common soil types in the Republic of Serbia—RS) and to compare their reliability to the basic models. From the total number of developed models by soil type (80), 75% were accepted as statistically reliable for predicting the bioavailability of TEs by soil type and 70% of prediction models had a higher determination coefficient (R2), compared to the basic models. For the Fluvisol soil type, all prediction models were accepted, while the least reliable prediction was for the Planosol type. As in the previous study of bioavailability prediction for TEs, the prediction models for Cu stood out, with more than half of the models with R2 greater than 0.90. Results of this study indicated that the formation of a testing set by soil type derives models whose predictions are more reliable than the basic ones. To improve the performance of prediction models, it is necessary to include additional physicochemical parameters and to conduct an adequate analysis of extensive testing sets with more comprehensive statistical techniques.

Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features

2021 ◽  
pp. 1-12
Author(s):  
Zongqiong Sun ◽  
Linfang Jin ◽  
Shuai Zhang ◽  
Shaofeng Duan ◽  
Wei Xing ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Clinical Features ◽  
Calibration Curve ◽  
Prediction Models ◽  
Ct Images ◽  
Clinical Findings ◽  
Training Set ◽  
Prediction Probability ◽  
Radiomics Signature ◽  
Testing Set

PURPOSE: To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS: The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS: In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p <  0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p >  0.05). CONCLUSION: The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.

scholarly journals Improvements and inter-laboratory implementation and optimization of blood-based single-locus age prediction models using DNA methylation of the ELOVL2 promoter

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Imene Garali ◽  
Mourad Sahbatou ◽  
Antoine Daunay ◽  
Laura G. Baudrin ◽  
Victor Renault ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Prediction Models ◽  
Single Locus ◽  
Training Set ◽  
Pooled Data ◽  
Locus Model ◽  
Age Dependent ◽  
Statistical Approaches ◽  
Age Prediction ◽  
Testing Set

Abstract Several blood-based age prediction models have been developed using less than a dozen to more than a hundred DNA methylation biomarkers. Only one model (Z-P1) based on pyrosequencing has been developed using DNA methylation of a single locus located in the ELOVL2 promoter, which is considered as one of the best age-prediction biomarker. Although multi-locus models generally present better performances compared to the single-locus model, they require more DNA and present more inter-laboratory variations impacting the predictions. Here we developed 17,018 single-locus age prediction models based on DNA methylation of the ELOVL2 promoter from pooled data of four different studies (training set of 1,028 individuals aged from 0 and 91 years) using six different statistical approaches and testing every combination of the 7 CpGs, aiming to improve the prediction performances and reduce the effects of inter-laboratory variations. Compared to Z-P1 model, three statistical models with the optimal combinations of CpGs presented improved performances (MAD of 4.41–4.77 in the testing set of 385 individuals) and no age-dependent bias. In an independent testing set of 100 individuals (19–65 years), we showed that the prediction accuracy could be further improved by using different CpG combinations and increasing the number of technical replicates (MAD of 4.17).

scholarly journals Identification of Susceptibility Genes in Hepatic Cancer Using Whole Exome Sequencing and Risk Prediction Model Construction

2020 ◽  
Vol 28 (1) ◽  
pp. 67-74
Author(s):  
Jinghui Zheng ◽  
Youming Tang ◽  
Encun Hou ◽  
Guangde Bai ◽  
Zuping Lian ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Risk Prediction ◽  
Prediction Models ◽  
Hepatic Cancer ◽  
Nucleotide Polymorphisms ◽  
Training Set ◽  
Whole Exome ◽  
Exon Sequence ◽  
Testing Set

AbstractObjective: To identify the susceptible single nucleotide polymorphisms (SNPs) loci in HCC patients in Guangxi Region, screen biomarkers from differential SNPs loci by using predictors, and establish risk prediction models for HCC, to provide a basis of screening high-risk individuals of HCC.Methods: Blood sample and clinical data of 50 normal participants and 50 hepatic cancer (HCC) patients in Rui Kang Hospital affiliated to Guangxi University of Traditional Chinese Medicine were collected. Normal participants and HCC patients were assigned to training set and testing set, respectively. Whole Exome Sequencing (WES) technique was employed to compare the exon sequence of the normal participants and HCC patients. Five predictors were used to screen the biomarkers and construct HCC prediction models. The prediction models were validated with both training and testing set.Results: Two-hundred seventy SNPs were identified to be significantly different from HCC, among which 100 SNPs were selected as biomarkers for prediction models. Five prediction models constructed with the 100 SNPs showed good sensitivity and specificity for HCC prediction among the training set and testing set.Conclusion: A series of SNPs were identified as susceptible genes for HCC. Some of these SNPs including CNN2, CD177, KMT2C, and HLADQB1 were consistent with the previously identified polymorphisms by targeted genes examination. The prediction models constructed with part of those SNPs could accurately predict HCC development.

scholarly journals Development And Assessment Of Machine Learning Models For Predicting Recurrence Risk After Endovascular Treatment In Patients With Intracranial Aneurysms

2021 ◽  
Author(s):  
Shiteng Lin ◽  
Yang Zou ◽  
Jue Hu ◽  
Lan Xiang ◽  
Leheng Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Endovascular Treatment ◽  
Intracranial Aneurysms ◽  
Prediction Models ◽  
Recurrence Risk ◽  
Health Concern ◽  
Gradient Boosting ◽  
Training Set ◽  
Interpretable Model ◽  
Testing Set

Abstract Intracranial aneurysms (IAs) remains a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and the data was randomly divided into a training set and a testing set. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months and the GBDT model showed the optimal prediction performance.

PROSPECTS FOR THE DEVELOPMENT OF BANKING INFORMATION TECHNOLOGIES IN THE REPUBLIC OF UZBEKISTAN

2020 ◽  
Vol 9 (3) ◽  
pp. 33-38
Author(s):  
Iroda Abdullaeva ◽  
◽  
Dilyora Hoshimova ◽  
Hamdam Xomidov ◽  
Maftuna Raxmonova
Keyword(s):  
Information System ◽  
Information Processing ◽  
Information Systems ◽  
Information Technologies ◽  
The Republic

This article is devoted to the prospects of the development of banking information systems in the Republic of Uzbekistan and highlights issues such as the processing of significant flows of information in the banking information system using advanced information processing tools

Interaction Process Model – ESM Quality Model Development Tool

2020 ◽  
Vol 25 (1) ◽  
pp. 10-17
Author(s):  
B.V. Boytsov ◽  
◽  
G.S. Zhetessova ◽  
M.K. Ibatov ◽  
◽  
...  
Keyword(s):  
Process Model ◽  
Swot Analysis ◽  
Statistical Data ◽  
Model Development ◽  
Interaction Process ◽  
Educational Process ◽  
Quality Model ◽  
Development Tool ◽  
The Republic ◽  
Education Science

The article discusses the methodology and provides the results of a multivariate SWOT analysis for a scientific and manufacturing educational process based on a set of regulatory and strategic documents, statistical data of the Republic of Kazakhstan; The main conclusions and description of the generated matrices for the subjects of interaction within the hierarchical triangle «Education – Science – Manufacturing (ESM)» are given.

Integrated clinicopathologic and molecular risk stratification for disease recurrence in muscle-invasive bladder cancer.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 490-490
Author(s):  
Ruben Carmona ◽  
Alan Pollack ◽  
Zachary L Smith ◽  
Jeff M. Michalski ◽  
Hiram Alberto Gay ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Stratification ◽  
Molecular Subtype ◽  
Disease Recurrence ◽  
Invasive Bladder Cancer ◽  
Training Set ◽  
Combined Model ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive ◽  
Testing Set

490 Background: Integrating molecular subtypes, gene transcripts associated with disease recurrence (DR), and clinicopathologic features may help risk stratify muscle-invasive bladder cancer (MIBC) patients & guide therapy selection. We hypothesized that combined transcriptomic & clinical data would improve risk stratification for DR (local or distant) after cystectomy +/- adjuvant chemotherapy. Methods: We identified 401 MIBC patients (pT2-4 N0-N3 M0) in The Cancer Genome Atlas with detailed demographic, clinical, pathologic, and treatment-related data. We split the data into training (60%) & testing (40%) sets. We produced RNA gene expression scores for molecular subtype using 48 established, relevant genes (PMID 28988769). In the training set, we performed feature selection by conducting random forest modeling of an additional 108 genes associated with DR. We kept genes of highest importance based on the evaluation of increasing mean-squared error & node purity. We excluded highly correlated genes & used the false discovery rate method for multiple hypotheses testing. We performed univariable analyses on genes of highest importance, molecular subtype, & clinicopathologic variables. Using adjusted multivariable analyses (MVA), we built two models: with & without transcriptomic data. Using the testing set, we compared the final models' performance to predict DR, using receiver operating characteristics & area under the curve (AUC). Results: Median follow-up was 18 months (range 1-168). 104 patients recurred with a 5-yr cumulative incidence of 34.6%[28.6-40.5%]. Using the training set, we identified 6 genes significantly associated with DR (VEGFA, TRMT1, FGFR2B, ERBB2, MMP14, PDGFC). The final MVA showed that the new 6-gene signature (HR 1.61, 95% CI 1.27-2.05, p < 0.001); immune molecular subtype [increased expression of PD-L1, PD-1, IDO1, CXCL11, L1CAM, SAA1] (HR 0.52, 95% CI 0.29-0.94, p = 0.03); smoking status (HR 1.17 per 10 pack-years, 95% CI 1.05-1.29, p = 0.005); and local failure risk factors [≥pT3 with negative margins & ≥10 nodes removed (HR 1.63, 95% CI 1.15-2.32, p = 0.006); ≥pT3 and positive margins OR < 10 nodes removed (HR 3.26, 95%CI 2.43 to 4.09, p = 0.007)], were all significantly associated with DR. This combined model outperformed a stand-alone clinicopathologic model (AUC 0.75 vs. 0.66) in the testing set. The combined model stratified patients based on DR risk into 3 groups with 5-yr cumulative incidences of 19.8%[7.7-31.9%] (low-risk); 34.5%[26.1-42.8%] (intermediate); and 49.8%[37.7-61.9%] (high), Gray’s Test p < 0.0001. Conclusions: To our knowledge, this study is the first to integrate clinicopathologic & transcriptomic information (including molecular subtype) to better stratify MIBC patients by risk of recurrence. This stratification may help guide decision-making for adjuvant treatment. Further validation is warranted.

scholarly journals Development and validation of clinical prediction models for mortality, functional outcome and cognitive impairment after stroke: a study protocol

BMJ Open ◽  
2017 ◽  
Vol 7 (8) ◽  
pp. e014607 ◽  
Author(s):  
Marion Fahey ◽  
Anthony Rudd ◽  
Yannick Béjot ◽  
Charles Wolfe ◽  
Abdel Douiri
Keyword(s):  
Cognitive Impairment ◽  
Functional Outcome ◽  
Multilevel Models ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
College Hospital ◽  
Stroke Registry ◽  
Temporal Validation ◽  
Stroke Register

IntroductionStroke is a leading cause of adult disability and death worldwide. The neurological impairments associated with stroke prevent patients from performing basic daily activities and have enormous impact on families and caregivers. Practical and accurate tools to assist in predicting outcome after stroke at patient level can provide significant aid for patient management. Furthermore, prediction models of this kind can be useful for clinical research, health economics, policymaking and clinical decision support.Methods2869 patients with first-ever stroke from South London Stroke Register (SLSR) (1995–2004) will be included in the development cohort. We will use information captured after baseline to construct multilevel models and a Cox proportional hazard model to predict cognitive impairment, functional outcome and mortality up to 5 years after stroke. Repeated random subsampling validation (Monte Carlo cross-validation) will be evaluated in model development. Data from participants recruited to the stroke register (2005–2014) will be used for temporal validation of the models. Data from participants recruited to the Dijon Stroke Register (1985–2015) will be used for external validation. Discrimination, calibration and clinical utility of the models will be presented.EthicsPatients, or for patients who cannot consent their relatives, gave written informed consent to participate in stroke-related studies within the SLSR. The SLSR design was approved by the ethics committees of Guy’s and St Thomas’ NHS Foundation Trust, Kings College Hospital, Queens Square and Westminster Hospitals (London). The Dijon Stroke Registry was approved by the Comité National des Registres and the InVS and has authorisation of the Commission Nationale de l’Informatique et des Libertés.

scholarly journals Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Prediction Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Heavy Rain ◽  
Machine Learning Techniques ◽  
Damage Prediction ◽  
Explanatory Variables ◽  
The Republic

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.

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