scholarly journals MutationTaster2021

2021 ◽  
Author(s):  
Robin Steinhaus ◽  
Sebastian Proft ◽  
Markus Schuelke ◽  
David N Cooper ◽  
Jana Marie Schwarz ◽  
...  
Keyword(s):  
Prediction Model ◽  
Clinical Phenotype ◽  
The Novel ◽  
User Friendliness ◽  
Major Overhaul ◽  
Splice Site Prediction ◽  
Disease Mutations ◽  
Many Sources ◽  
Variant Effect Prediction ◽  
Mutation Search

Abstract Here we present an update to MutationTaster, our DNA variant effect prediction tool. The new version uses a different prediction model and attains higher accuracy than its predecessor, especially for rare benign variants. In addition, we have integrated many sources of data that only became available after the last release (such as gnomAD and ExAC pLI scores) and changed the splice site prediction model. To more easily assess the relevance of detected known disease mutations to the clinical phenotype of the patient, MutationTaster now provides information on the diseases they cause. Further changes represent a major overhaul of the interfaces to increase user-friendliness whilst many changes under the hood have been designed to accelerate the processing of uploaded VCF files. We also offer an API for the rapid automated query of smaller numbers of variants from within other software. MutationTaster2021 integrates our disease mutation search engine, MutationDistiller, to prioritise variants from VCF files using the patient's clinical phenotype. The novel version is available at https://www.genecascade.org/MutationTaster2021/. This website is free and open to all users and there is no login requirement.

Novel somatic mutations in UBA1 as a cause of VEXAS syndrome

Blood ◽  
2021 ◽  
Author(s):  
James A Poulter ◽  
Jason Charles Collins ◽  
Catherine Cargo ◽  
Ruth M de Tute ◽  
Paul Evans ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Somatic Mutations ◽  
Clinical Phenotype ◽  
Splice Acceptor Site ◽  
Acceptor Site ◽  
The Novel ◽  
Disease Mechanism ◽  
Bone Marrow Biopsies ◽  
Novel Variant

Somatic mutations at methionine 41 (Met41) in UBA1, encoding the major E1 enzyme responsible for initiating ubiquitylation, were recently identified as the cause of a novel autoinflammatory disease, named VEXAS (Vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic). We sought to determine the prevalence of UBA1 mutations in a UK cohort of patients matching the VEXAS clinical phenotype. We identified 10 new patients with somatic mutations in UBA1, but only 8 had altered p.Met41. A novel variant, c.167C>T; p.Ser56Phe was identified, which was present in myeloid, and not lymphoid lineages and led to preferential loss of the catalytic activity of cytoplasmic UBA1. An additional novel variant, c.118-1G>C was identified at the splice acceptor site of exon 3 leading to altered splicing in vitro. Bone marrow biopsies from two patients with a Met41 substitution and the novel splice site variant were consistent with previously reported features of VEXAS. The bone marrow of the patient with the p.Ser56Phe variant was less similar, likely driven by a distinct but overlapping disease mechanism. Our study therefore confirms somatic p.Met41 substitutions in UBA1 as a major cause of VEXAS syndrome and identifies two new disease causing mutations.

scholarly journals An optimized industry processing technology of peanut tofu and the novel prediction model for suitable peanut varieties

2020 ◽  
Vol 19 (9) ◽  
pp. 2340-2351
Author(s):  
Bing-yu CHEN ◽  
Qi-zhai LI ◽  
Hui HU ◽  
Shi MENG ◽  
Faisal SHAH ◽  
...  
Keyword(s):  
Prediction Model ◽  
Processing Technology ◽  
The Novel

Mosaic Generalized Neurofibromatosis 1: Report of Two Cases

2014 ◽  
Vol 18 (4) ◽  
pp. 271-274 ◽  
Author(s):  
Jori Hardin ◽  
Allan Behm ◽  
Richard M. Haber
Keyword(s):  
Somatic Mutations ◽  
Clinical Phenotype ◽  
Neurofibromatosis Type ◽  
Neurofibromatosis 1 ◽  
Limited Disease ◽  
Disease Mutations ◽  
Single Region ◽  
History Of

Background: We report two cases of mosaic generalized neurofibromatosis 1 (NF1) and review the history of the classification of segmental neurofibromatosis (SNF; Ricardi type NF-V). Somatic mutations giving rise to limited disease, such as segmental neurofibromatosis are manifestations of mosaicism. If the mutation occurs before tissue differentiation, the clinical phenotype will be generalized disease. Mutations that occur later in development give rise to disease that is confined to a single region. Objectives: Segmental neurofibromatosis is caused by a somatic mutation of neurofibromatosis type 1, and should not be regarded as a distinct entity from neurofibromatosis 1. Cases previously referred to as unilateral or bilateral segmental neurofibromatosis are now best referred to as mosaic generalized or mosaic localized neurofibromatosis 1.

scholarly journals A Novel Extrapolation-Based Grey Prediction Model for Forecasting China’s Total Electricity Consumption

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin-bo Yang
Keyword(s):  
Prediction Model ◽  
Extreme Values ◽  
Model Performance ◽  
Electricity Consumption ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Background Value ◽  
Development Policies ◽  
The Government

Accurately forecasting China’s total electricity consumption is of great significance for the government in formulating sustainable economic development policies, especially, China as the largest total electricity consumption country in the world. The calculation method of the background value of the GM(1, 1) model is an important factor of unstable model performance. In this paper, an extrapolation method with variable weights was used for calculating the background value to eliminate the influence of the extreme values on the performance of the GM(1, 1) model, and the novel extrapolation-based grey prediction model called NEGM(1, 1) was proposed and optimized. The NEGM(1, 1) model was then used to simulate the total electricity consumption in China and found to outperform other grey models. Finally, the total electricity consumption of China from 2018 to 2025 was forecasted. The results show that China’s total electricity consumption will be expected to increase slightly, but the total is still very large. For this, some corresponding recommendations to ensure the effective supply of electricity in China are suggested.

scholarly journals An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

2020 ◽  
Vol 10 (4) ◽  
pp. 280-292
Author(s):  
Allemar Jhone P. Delima
Keyword(s):  
Genetic Algorithm ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Prediction Models ◽  
Faculty Evaluation ◽  
The Novel ◽  
Improved Genetic Algorithm ◽  
K Nearest Neighbor ◽  
Negative Effects

The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improved genetic algorithm (CIGAL) is instrumental in the enhancement of KNN’s prediction accuracy. The use of the unmodified genetic algorithm has removed 13 variables, while the CIGAL then further removes 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improves the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN), the use of the lone KNN algorithmand the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique reveals 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. As the result, the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.

scholarly journals Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoshuang Luo ◽  
Bo Zeng ◽  
Hui Li ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Models ◽  
Grey Model ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Combination Optimization ◽  
Installed Capacity ◽  
Novel Model

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

scholarly journals Optimization Model Based on Multivariable Grey Model and Its Application to Energy Consumption in China

2021 ◽  
Author(s):  
Kai Xu ◽  
Xilin Luo ◽  
Xinyu Pang
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Prediction Model ◽  
Prediction Models ◽  
Clean Energy ◽  
Energy Development ◽  
The Other ◽  
The Novel ◽  
Grey Prediction ◽  
Novel Model

Abstract Currently, the energy development in China is in a critical period of transformation and reform, facing unprecedented opportunities and challenges. Accurate energy consumption forecast is conducive to promoting the diversification of energy development and utilization, and ensuring the healthy and rapid development of China's economy. Based on the existing multivariable grey prediction model, a nonlinear multivariable grey prediction model with parameter optimization is established in this paper, which used the genetic algorithms to find the optimal parameters, and the modelling steps are obtained. Then, the novel model takes the oil natural gas, coal and clean energy in China as the research objects, and the results are compared with the other four grey prediction models. The novel model has higher simulation and prediction accuracy, which is better than the other four grey prediction models. Finally, the novel model is used to predict those four energy consumption forecasts in China from 2020 to 2024. The results show that various energy consumption will further increase, while the fastest growing is clean energy and natural gas, which provides effective information for the Chinese government to formulate energy economic policies.

The Analysis of Combined Prediction Model of Building Energy Consumption with Grey Theory and RBF Neural Network

2011 ◽  
Vol 374-377 ◽  
pp. 90-93
Author(s):  
Yan Bai ◽  
Qing Chang Ren ◽  
Hong Mei Jiang
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Rbf Neural Network ◽  
Grey Theory ◽  
The Novel ◽  
Grey Prediction ◽  
Consumption Data ◽  
Building Area ◽  
Simulation And Experiment

A kind of new combined modeling method with GM(1,1) and RBNN (Radial Basis Neural Network) is brought forward, according to the idea that the method of neural network can bring grey prediction model a good modified effect. Based on the analysis of the energy consumption data of the existing and the annually-increased building area, the GM(1,1) model was then constructed. And the RBF neural network was used for the model residual error revising. The simulation and experiment results show that the novel model is more effective than the common grey model.

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