scholarly journals Detection of de novo copy number deletions from targeted sequencing of trios

2018 ◽  
Author(s):  
Jack M. Fu ◽  
Elizabeth J. Leslie ◽  
Alan F. Scott ◽  
Jeffrey C. Murray ◽  
Mary L. Marazita ◽  
...  
Keyword(s):  
Open Source Software ◽  
Copy Number ◽  
Minimum Distance ◽  
Formal Method ◽  
De Novo ◽  
False Positive Rate ◽  
Targeted Sequencing ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Sequencing Platforms

AbstractDe novo copy number deletions have been implicated in many diseases, but there is no formal method to date however that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms. We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific CNV callers, resulting in a much higher positive predictive value (precision). MDTS also exhibited much better scalability, and is available as open source software at github.com/JMF47/MDTS.

scholarly journals Implementation of maternal blood cell-free DNA testing in early screening for aneuploidies

2019 ◽  
Vol 31 (3) ◽  
pp. 283-288
Author(s):  
María M. Gil ◽  
Kypros H. Nicolaides
Keyword(s):  
False Positive Rate ◽  
Maternal Blood ◽  
Screening Methods ◽  
Routine Practice ◽  
Clinical Implementation ◽  
Early Screening ◽  
Test Models ◽  
Lower False Positive Rate ◽  
Implementation Studies ◽  
Positive Rate

AbstractSeveral externally blinded validation and implementation studies in the last 9 years have shown that it is now possible, through analysis of cell-free (cf) DNA in maternal blood, to effectively detect a high proportion of fetuses affected by trisomies 21, 18, and 13 at a much lower false-positive rate (FPR) than all other existing screening methods. This article is aimed at reviewing technical and clinical considerations for implementing cfDNA testing in routine practice, including methods of analysis, performance of the test, models for clinical implementation, and interpretation of results.

scholarly journals A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1210 ◽  
Author(s):  
Khraisat ◽  
Gondal ◽  
Vamplew ◽  
Kamruzzaman ◽  
Alazab
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Support Vector ◽  
Detection Accuracy ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Iot Devices

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

An efficient SAR edge detector with a lower false positive rate

2015 ◽  
Vol 36 (14) ◽  
pp. 3773-3797 ◽  
Author(s):  
Qian-Ru Wei ◽  
Da-Zheng Feng
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Edge Detector ◽  
Lower False Positive Rate ◽  
Positive Rate

A Novel Hybrid LE and SVM with CV in Intrusion Detection

2014 ◽  
Vol 644-650 ◽  
pp. 2572-2576
Author(s):  
Qing Liu ◽  
Yun Kai Zhang ◽  
Qing Ru Li
Keyword(s):  
Intrusion Detection ◽  
False Positive Rate ◽  
Likelihood Estimation ◽  
Support Vector ◽  
Training Time ◽  
Detection Algorithms ◽  
Lower False Positive Rate ◽  
Proposed Model ◽  
Positive Rate ◽  
Rbf Kernel

A support vector machine (SVM) model combined Laplacian Eigenmaps (LE) with Cross Validation (CV) is proposed for intrusion detection. In the proposed model, a classifier is adopted to estimate whether an action is an attack or not. Maximum Likelihood Estimation (MLE) is used to estimate the intrinsic dimensions, and LE is used as a preprocessor of SVM to reduce the dimensions of feature vectors then training time is shortened. In order to improve the performance of SVM, CV is used to optimize the parameters of SVM in RBF kernel function. Compared with other detection algorithms, the experimental results show that the proposed model has the advantages: shorter training time, higher accuracy rate and lower false positive rate.

scholarly journals P07.05: Using nasal bone as a marker is associated with lower false-positive rate in sonographic screening of trisomy 21 at Hospital Sonderjylland Sonderborg

2012 ◽  
Vol 40 (S1) ◽  
pp. 194-194
Author(s):  
R. Andreassen ◽  
L. Brendstrup ◽  
D. Schmidt ◽  
M. Bojsen ◽  
K. Sievertsen ◽  
...  
Keyword(s):  
False Positive ◽  
Trisomy 21 ◽  
False Positive Rate ◽  
Nasal Bone ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Sonographic Screening

scholarly journals Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Chin-Fu Liu ◽  
Johnny Hsu ◽  
Xin Xu ◽  
Sandhya Ramachandran ◽  
Victor Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
False Positive Rate ◽  
Learning Networks ◽  
Large Dataset ◽  
Lower False Positive Rate ◽  
Proposed Model ◽  
Positive Rate ◽  
Total Agreement ◽  
Clinical And Translational Research

Abstract Background Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

scholarly journals Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning

2019 ◽  
Vol 2019 (4) ◽  
pp. 292-310 ◽  
Author(s):  
Sanjit Bhat ◽  
David Lu ◽  
Albert Kwon ◽  
Srinivas Devadas
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Training Data ◽  
Open World ◽  
Prior Art ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Fingerprinting Attack

Abstract In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over 1% higher true positive rate (TPR) than state-of-the-art attacks while achieving 4× lower false positive rate (FPR). Var-CNN’s improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by 3.12% while increasing the TPR by 13%. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues.

Abstract 17015: When is a Pathological Q Wave Truly Pathological?

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Keerthi Prakash ◽  
Aneil Malhotra ◽  
Harshil Dhutia ◽  
Philippe Siegenthaler ◽  
Grant Nolan ◽  
...  
Keyword(s):  
False Positive ◽  
Comprehensive Evaluation ◽  
False Positive Rate ◽  
Young Athletes ◽  
Entire Cohort ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Cardiac Evaluation ◽  
Health And Disease ◽  
Q Wave

Introduction: Pathological Q waves are considered harbingers of cardiac pathology and should instigate comprehensive evaluation of athletes. Several definitions of the depth or duration of a Q wave exist, with disagreement between scientific bodies as to the most useful. Objectives: 1. Determine the prevalence of pathological Q waves in large cohorts of the general population, athletes and patients with hypertrophic cardiomyopathy (HCM). 2. Identify the most accurate Q wave criterion. Methods: ECGs were retrospectively analysed in consecutive cohorts of 10,008 healthy young athletes (14-35 years old), 2,994 healthy young non-athletes and 468 HCM patients. Results: Pathological Q waves that fulfilled at least one of the 4 individual definitions (Table 1) were identified in 0.7% athletes (n = 75), 1.2% non-athletes (n = 36) and 22.6% patients with HCM (n = 106). In the healthy athletic and non-athletic population, all pathological Q waves (n = 111) met the >3mm depth definition. In contrast, the majority of pathological Q waves in HCM patients met the ≥40msec duration definition (n = 75; 70.75%). We tested the ability of all 4 criteria to distinguish between health and disease in the entire cohort. Seattle and Refined had the best sensitivity for detecting HCM. Refined criteria however, had a significantly higher positive predictive value and 7 times lower false positive rate compared to Seattle. (Table 2) Conclusion: Pathological Q waves are present in up to 1.2% of healthy young individuals and do not correlate with physical activity. Of the proposed criteria, the Refined criteria has the lowest false positive rate and should be utilised in the context of cardiac evaluation in young athletes.

Automated linkage of patient records from disparate sources

2016 ◽  
Vol 27 (1) ◽  
pp. 172-184
Author(s):  
Xiaochun Li ◽  
Huiping Xu ◽  
Changyu Shen ◽  
Shaun Grannis
Keyword(s):  
Data Quality ◽  
Model Simulation ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Automated Method ◽  
Discriminating Power ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
The Face ◽  
Biased Estimates

We introduce an automated method of record linkage that has two key features, automated selection of match field interactions to include in the model for estimation and automated threshold determination for classifying record pairs to matches or non-matches. We applied our method to two real-world examples. The first example demonstrated results consistent with our earlier work: When data quality is adequate and the match field discriminating power is high, matching algorithms exhibit similar performance. The second example demonstrated that our method yields a lower false positive rate and higher positive predictive value than the Fellegi-Sunter model in the face of low data quality. When compared to the Fellegi-Sunter model, simulation studies suggest that our method exhibits better overall performance as indicated by higher area under the curve, and less biased estimates for both the match prevalence rate and the m- and u-probabilities over a range of data scenarios, especially when the match prevalence is extreme. Computationally, our method is as efficient as the Fellegi-Sunter model. We recommend this method in situations that an unsupervised linking algorithm is needed.

Needle EMG has a lower false positive rate than MRI in asymptomatic older adults being evaluated for lumbar spinal stenosis

2007 ◽  
Vol 118 (4) ◽  
pp. 751-756 ◽  
Author(s):  
Anthony Chiodo ◽  
Andrew J. Haig ◽  
Karen S.J. Yamakawa ◽  
Douglas Quint ◽  
Henry Tong ◽  
...  
Keyword(s):  
Older Adults ◽  
Spinal Stenosis ◽  
Lumbar Spinal Stenosis ◽  
False Positive ◽  
False Positive Rate ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Lumbar Spinal
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