scholarly journals Assessing performance of pathogenicity predictors using clinically relevant variant datasets

2020 ◽  
pp. jmedgenet-2020-107003
Author(s):  
Adam C Gunning ◽  
Verity Fryer ◽  
James Fasham ◽  
Andrew H Crosby ◽  
Sian Ellard ◽  
...  
Keyword(s):  
In Silico ◽  
Operating Characteristic ◽  
Current Practice ◽  
Characteristic Curve ◽  
Variant Interpretation ◽  
Independent Validation ◽  
Tool Performance ◽  
Genomic Variant ◽  
Insight Into ◽  
In Silico Tools

BackgroundPathogenicity predictors are integral to genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically relevant dataset has not been undertaken.MethodsWe derive two validation datasets: an ‘open’ dataset containing variants extracted from publicly available databases, similar to those commonly applied in previous benchmarking exercises, and a ‘clinically representative’ dataset containing variants identified through research/diagnostic exome and panel sequencing. Using these datasets, we evaluate the performance of three recent meta-predictors, REVEL, GAVIN and ClinPred, and compare their performance against two commonly used in silico tools, SIFT and PolyPhen-2.ResultsAlthough the newer meta-predictors outperform the older tools, the performance of all pathogenicity predictors is substantially lower in the clinically representative dataset. Using our clinically relevant dataset, REVEL performed best with an area under the receiver operating characteristic curve of 0.82. Using a concordance-based approach based on a consensus of multiple tools reduces the performance due to both discordance between tools and false concordance where tools make common misclassification. Analysis of tool feature usage may give an insight into the tool performance and misclassification.ConclusionOur results support the adoption of meta-predictors over traditional in silico tools, but do not support a consensus-based approach as in current practice.

scholarly journals Assessing performance of pathogenicity predictors using clinically-relevant variant datasets

2020 ◽  
Author(s):  
Adam C Gunning ◽  
Verity Fryer ◽  
James Fasham ◽  
Andrew H Crosby ◽  
Sian Ellard ◽  
...  
Keyword(s):  
In Silico ◽  
Variant Classification ◽  
Variant Interpretation ◽  
Independent Validation ◽  
Tool Performance ◽  
Diagnostic Panel ◽  
Genomic Variant ◽  
Insight Into ◽  
Panel Sequencing

ABSTRACTPurposePathogenicity predictors are an integral part of genomic variant interpretation but, despite their widespread usage, an independent validation of performance using a clinically-relevant dataset has not been undertaken.MethodsWe derive two validation datasets: an “open” dataset containing variants extracted from publicly-available databases, similar to those commonly applied in previous benchmarking exercises, and a “clinically-representative” dataset containing variants identified through research/diagnostic exome and diagnostic panel sequencing. Using these datasets, we evaluate the performance of three recently developed meta-predictors, REVEL, GAVIN and ClinPred, and compare their performance against two commonly used in silico tools, SIFT and PolyPhen-2.ResultsAlthough the newer meta-predictors outperform the older tools, the performance of all pathogenicity predictors is substantially lower in the clinically-representative dataset. Using our clinically-relevant dataset, REVEL performed best with an area under the ROC of 0.81. Using a concordance-based approach based on a consensus of multiple tools reduces the performance due to both discordance between tools and false concordance where tools make common misclassification. Analysis of tool feature usage may give an insight into the tool performance and misclassification.ConclusionOur results support the adoption of meta-predictors over traditional in silico tools, but do not support a consensus-based approach as recommended by current variant classification guidelines.

scholarly journals BRCA1 ‐ and BRCA2 ‐specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge

2019 ◽  
Vol 40 (9) ◽  
pp. 1593-1611 ◽  
Author(s):  
Natàlia Padilla ◽  
Alejandro Moles‐Fernández ◽  
Casandra Riera ◽  
Gemma Montalban ◽  
Selen Özkan ◽  
...  
Keyword(s):  
In Silico ◽  
Variant Interpretation ◽  
Brca1 And Brca2 ◽  
In Silico Tools

scholarly journals Analysis of Roadway Traffic Accidents Based on Rough Sets and Bayesian Networks

2018 ◽  
Vol 30 (1) ◽  
pp. 71-81 ◽  
Author(s):  
Xiaoxia Xiong ◽  
Long Chen ◽  
Jun Liang
Keyword(s):  
Bayesian Networks ◽  
Rough Sets ◽  
Traffic Accidents ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Traffic Density ◽  
Secondary Tasks ◽  
Knowledge Reduction ◽  
Naturalistic Driving ◽  
Insight Into

The paper integrates Rough Sets (RS) and Bayesian Networks (BN) for roadway traffic accident analysis. RS reduction of attributes is first employed to generate the key set of attributes affecting accident outcomes, which are then fed into a BN structure as nodes for BN construction and accident outcome classification. Such RS-based BN framework combines the advantages of RS in knowledge reduction capability and BN in describing interrelationships among different attributes. The framework is demonstrated using the 100-car naturalistic driving data from Virginia Tech Transportation Institute to predict accident type. Comparative evaluation with the baseline BNs shows the RS-based BNs generally have a higher prediction accuracy and lower network complexity while with comparable prediction coverage and receiver operating characteristic curve area, proving that the proposed RS-based BN overall outperforms the BNs with/without traditional feature selection approaches. The proposed RS-based BN indicates the most significant attributes that affect accident types include pre-crash manoeuvre, driver’s attention from forward roadway to centre mirror, number of secondary tasks undertaken, traffic density, and relation to junction, most of which feature pre-crash driver states and driver behaviours that have not been extensively researched in literature, and could give further insight into the nature of traffic accidents.

scholarly journals Clinical Value and Underlying Mechanisms of Upregulated LINC00485 in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xinyu Zhu ◽  
Yanlin Feng ◽  
Dingdong He ◽  
Zi Wang ◽  
Fangfang Huang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Underlying Mechanism ◽  
Clinical Value ◽  
Serum Samples ◽  
Underlying Mechanisms ◽  
Insight Into

AimsThis study aimed to reveal the functional role of LINC00485 in hepatocellular carcinoma (HCC).Materials & Methods210 serum samples from Zhongnan Hospital of Wuhan University were employed to evaluate clinical value of LINC00485. Bioinformatics analysis was adopted to explore its potential mechanisms.ResultsLINC00485 was confirmed to be upregulated in HCC tissues and serum samples. Survival analysis and receiver operating characteristic curve revealed its prognostic and diagnostic roles. The combination of serum LINC00485 with AFP can remarkably improve diagnostic ability of HCC. Exploration of the underlying mechanism demonstrated that LINC00485 might exert pro-oncogenic activity by LINC00485—three miRNAs—four mRNAs network.ConclusionsOur study unveiled that upregulated LINC00485 could act as a potential diagnostic and prognostic biomarker and provide a novel insight into the molecular mechanisms of LINC00485 in HCC pathogenesis.

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

An Update of In Silico Tools for the Prediction of Pathogenesis in Missense Variants

2011 ◽  
Vol 6 (2) ◽  
pp. 185-198
Author(s):  
Alejandro j. Brea-Fernandez ◽  
Marta Ferro ◽  
Ceres Fernandez-Rozadilla ◽  
Ana Blanco ◽  
Laura Fachal ◽  
...  
Keyword(s):  
In Silico ◽  
Missense Variants ◽  
In Silico Tools

Serum miR-21 and miR-26a Levels Negatively Correlate with Severity of Cirrhosis in Patients with Chronic Hepatitis B

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Shili Jiang ◽  
Wei Jiang ◽  
Ying Xu ◽  
Xiaoning Wang ◽  
Yongping Mu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Pugh Class ◽  
Operating Characteristic Curve ◽  
Class C ◽  
Circulating Levels

Background and Objective: Accurately evaluating the severity of liver cirrhosis is essential for clinical decision making and disease management. This study aimed to evaluate the value of circulating levels of microRNA (miR)-26a and miR-21 as novel noninvasive biomarkers in detecting severity of cirrhosis in patients with chronic hepatitis B. </P><P> Methods: Thirty patients with clinically diagnosed chronic hepatitis B-related cirrhosis and 30 healthy individuals were selected. The serum levels of miR-26a and miR-21 were quantified by qRT-PCR. Receiver operating characteristic curve analysis was performed to evaluate the sensitivity and specificity of the miRNAs for detecting the severity of cirrhosis. Results: Serum miR-26a and miR-21 levels were found to be significantly downregulated in patients with severe cirrhosis scored at Child-Pugh class C in comparison to healthy controls (miR-26a p<0.01, and miR-21 p<0.001, respectively). The circulating miR-26a and miR-21 levels in patients were positively correlated with serum albumin concentration but negatively correlated with serum total bilirubin concentration and prothrombin time. Receiver operating characteristic curve analysis revealed that both serum miR-26a and miR-21 levels were associated with a high diagnostic accuracy for patients with cirrhosis scored at Child-Pugh class C (miR-26a Cut-off fold change at ≤0.4, Sensitivity: 84.62%, Specificity: 89.36%, P<0.0001; miR-21 Cut-off fold change at ≤0.6, Sensitivity: 84.62%, Specificity: 78.72%, P<0.0001). Our results indicate that the circulating levels of miR-26a and miR-21 are closely related to the extent of liver decompensation, and the decreased levels are capable of discriminating patients with cirrhosis at Child-Pugh class C from the whole cirrhosis cases.

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