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Background Multiple different evidence types as well as gene-specific variant classification guidelines need to be considered during the classification of variants, making the process complex. Therefore, tools that support variant classification by experts are urgently needed. Methods We present HerediVar a web application and HerediClassify a variant classification algorithm. The performance of HerediClassify was validated and compared to other variant classification tools. HerediClassify implements 19/28 variant classification criteria by the American College of Medical Genetics and gene-specific recommendations for ATM , BRCA1 , BRCA2 , CDH1 , PALB2 , PTEN , and TP53 . Results HerediVar offers modular annotation services and allows for collaboration in the classification of variants. On the validation dataset, HerediClassify shows an average F1-Score of 93% across all criteria. HerediClassify outperforms other automated variant classification tools like vaRHC and Cancer SIGVAR. Conclusion In HerediVar and HerediClassify we present a powerful solution to support variant classification in HBOC. Through their modular design, HerediVar and HerediClassify are easily extendable to other use cases and human genetic diagnostics as a whole.

Background Automation has been introduced into variant interpretation, but it is not known how automated variant interpretation performs on a stand‐alone basis. The purpose of this study was to evaluate a fully automated computerized approach. Method We reviewed all variants encountered in a set of carrier screening panels over a 1‐year interval. Observed variants with high‐confidence ClinVar interpretations were included in the analysis; those without high‐confidence ClinVar entries were excluded. Results Discrepancy rates between automated interpretations and high‐confidence ClinVar entries were analyzed. Of the variants interpreted as positive (likely pathogenic or pathogenic) based on ClinVar information, 22.6% were classified as negative (variants of uncertain significance, likely benign or benign) variants by the automated method. Of the ClinVar negative variants, 1.7% were classified as positive by the automated software. On a per‐case basis, which accounts for variant frequency, 63.4% of cases with a ClinVar high‐confidence positive variant were classified as negative by the automated method. Conclusion While automation in genetic variant interpretation holds promise, there is still a need for manual review of the output. Additional validation of automated variant interpretation methods should be conducted. Here, we performed a comparative analysis of a fully automated curation of variant classification versus a process that included an additional manual component. The comparison was carried out for a set of variants where there was high confidence in their pathogenic classification, based on ClinVar entries. We found that a high proportion of automated interpretations (22.6% of positive and 1.7% of negative variants) were reclassified when there was a manual review. We conclude that manual review of the output from the automated variant classifier is currently essential.

We present Gene-Aware Variant INterpretation (GAVIN), a new method that accurately classifies variants for clinical diagnostic purposes. Classifications are based on gene-specific calibrations of allele frequencies from the ExAC database, likely variant impact using SnpEff, and estimated deleteriousness based on CADD scores for >3000 genes. In a benchmark on 18 clinical gene sets, we achieve a sensitivity of 91.4% and a specificity of 76.9%. This accuracy is unmatched by 12 other tools. We provide GAVIN as an online MOLGENIS service to annotate VCF files and as an open source executable for use in bioinformatic pipelines. It can be found at http://molgenis.org/gavin .

Banking malware poses a significant threat to users by infecting their computers and then attempting to perform malicious activities such as surreptitiously stealing confidential information from them. Banking malware variants are also continuing to evolve and have been increasing in numbers for many years. Amongst these, the banking malware Zeus and its variants are the most prevalent and widespread banking malware variants discovered. This prevalence was expedited by the fact that the Zeus source code was inadvertently released to the public in 2004, allowing malware developers to reproduce the Zeus banking malware and develop variants of this malware. Examples of these include Ramnit, Citadel, and Zeus Panda. Tools such as anti-malware programs do exist and are able to detect banking malware variants, however, they have limitations. Their reliance on regular updates to incorporate new malware signatures or patterns means that they can only identify known banking malware variants. This constraint inherently restricts their capability to detect novel, previously unseen malware variants. Adding to this challenge is the growing ingenuity of malicious actors who craft malware specifically developed to bypass signature-based anti-malware systems. This paper presents an overview of the Zeus, Zeus Panda, and Ramnit banking malware variants and discusses their communication architecture. Subsequently, a methodology is proposed for detecting banking malware C&C communication traffic, and this methodology is tested using several feature selection algorithms to determine which feature selection algorithm performs the best. These feature selection algorithms are also compared with a manual feature selection approach to determine whether a manual, automated, or hybrid feature selection approach would be more suitable for this type of problem.

Banking malware poses a significant threat to users by infecting their computers and then attempting to perform malicious activities such as surreptitiously stealing confidential information from them. Banking malware variants are also continuing to evolve and have been increasing in numbers for many years. Amongst these, the banking malware Zeus and its variants are the most prevalent and widespread banking malware variants discovered. This prevalence was expedited by the fact that the Zeus source code was inadvertently released to the public in 2004, allowing malware developers to reproduce the Zeus banking malware and develop variants of this malware. Examples of these include Ramnit, Citadel, and Zeus Panda. Tools such as anti-malware programs do exist and are able to detect banking malware variants, however, they have limitations. Their reliance on regular updates to incorporate new malware signatures or patterns means that they can only identify known banking malware variants. This constraint inherently restricts their capability to detect novel, previously unseen malware variants. Adding to this challenge is the growing ingenuity of malicious actors who craft malware specifically developed to bypass signature-based anti-malware systems. This paper presents an overview of the Zeus, Zeus Panda, and Ramnit banking malware variants and discusses their communication architecture. Subsequently, a methodology is proposed for detecting banking malware C&C communication traffic, and this methodology is tested using several feature selection algorithms to determine which feature selection algorithm performs the best. These feature selection algorithms are also compared with a manual feature selection approach to determine whether a manual, automated, or hybrid feature selection approach would be more suitable for this type of problem.

Today, along with the development of the Internet, the number of malicious software, or malware, distributed especially for monetary profits, is exponentially increasing, and malware authors are developing malware variants using various automated tools and methods. Automated tools and methods may reuse some modules to develop malware variants, so these reused modules can be used to classify malware or to identify malware families. Therefore, similarities may exist among malware variants can be analyzed and used for malware variant detections and the family classification. This paper proposes a new malware family classification method by converting binary files into images and entropy graphs. The experimental results show that the proposed method can effectively distinguish malware families.

Background Variant curation refers to the application of evidence‐based methods for the interpretation of genetic variants. Significant variability in this process among laboratories affects clinical practice. For admixed Hispanic/Latino populations, underrepresented in genomic databases, the interpretation of genetic variants for cancer risk is challenging. Methods We retrospectively evaluated 601 sequence variants detected in patients participating in the largest Institutional Hereditary Cancer Program in Colombia. VarSome and PathoMAN were used for automated curation, and ACMG/AMP and Sherloc criteria were applied for manual curation. Results Regarding the automated curation, 11% of the variants (64/601) were reclassified, 59% (354/601) had no changes in its interpretation, and the other 30% (183/601) presented conflicting interpretations. With respect to manual curation, of the 183 variants with conflicting interpretations, 17% (N = 31) were reclassified, 66% (N = 120) had no changes in their initial interpretation, and 17% (N = 32) remained with conflicting interpretation status. Overall, 91% of the VUS were downgraded and 9% were upgraded. Conclusions Most VUS were reclassified as benign/likely benign. Since false‐positive and ‐negative results can be obtained with automated tools, manual curation should also be used as a complement. Our results contribute to improving cancer risk assessment and management for a broad range of hereditary cancer syndromes in Hispanic/Latino populations. An automated and manual curation workflow allows us to reclassify 16% (95/601) of the genetic variants identified in a Latin American population with a suspected hereditary cancer syndrome. VUS corresponded to 71% of the reclassified variants; from these, 91% were downgraded to B/LB and only 9% were upgraded to P/LP. Our results contribute to improving cancer risk assessment and management for a broad range of hereditary cancer syndromes in Hispanic/Latino populations.

PurposeThis work aims to present a deep learning model for face mask detection in surveillance environments such as automatic teller machines (ATMs), banks, etc. to identify persons wearing face masks. In surveillance environments, complete visibility of the face area is a guideline, and criminals and law offenders commit crimes by hiding their faces behind a face mask. The face mask detector model proposed in this work can be used as a tool and integrated with surveillance cameras in autonomous surveillance environments to identify and catch law offenders and criminals.Design/methodology/approachThe proposed face mask detector is developed by integrating the residual network (ResNet)34 feature extractor on top of three You Only Look Once (YOLO) detection layers along with the usage of the spatial pyramid pooling (SPP) layer to extract a rich and dense feature map. Furthermore, at the training time, data augmentation operations such as Mosaic and MixUp have been applied to the feature extraction network so that it can get trained with images of varying complexities. The proposed detector is trained and tested over a custom face mask detection dataset consisting of 52,635 images. For validation, comparisons have been provided with the performance of YOLO v1, v2, tiny YOLO v1, v2, v3 and v4 and other benchmark work present in the literature by evaluating performance metrics such as precision, recall, F1 score, mean average precision (mAP) for the overall dataset and average precision (AP) for each class of the dataset.FindingsThe proposed face mask detector achieved 4.75–9.75 per cent higher detection accuracy in terms of mAP, 5–31 per cent higher AP for detection of faces with masks and, specifically, 2–30 per cent higher AP for detection of face masks on the face region as compared to the tested baseline variants of YOLO. Furthermore, the usage of the ResNet34 feature extractor and SPP layer in the proposed detection model reduced the training time and the detection time. The proposed face mask detection model can perform detection over an image in 0.45 s, which is 0.2–0.15 s lesser than that for other tested YOLO variants, thus making the proposed detection model perform detections at a higher speed.Research limitations/implicationsThe proposed face mask detector model can be utilized as a tool to detect persons with face masks who are a potential threat to the automatic surveillance environments such as ATMs, banks, airport security checks, etc. The other research implication of the proposed work is that it can be trained and tested for other object detection problems such as cancer detection in images, fish species detection, vehicle detection, etc.Practical implicationsThe proposed face mask detector can be integrated with automatic surveillance systems and used as a tool to detect persons with face masks who are potential threats to ATMs, banks, etc. and in the present times of COVID-19 to detect if the people are following a COVID-appropriate behavior of wearing a face mask or not in the public areas.Originality/valueThe novelty of this work lies in the usage of the ResNet34 feature extractor with YOLO detection layers, which makes the proposed model a compact and powerful convolutional neural-network-based face mask detector model. Furthermore, the SPP layer has been applied to the ResNet34 feature extractor to make it able to extract a rich and dense feature map. The other novelty of the present work is the implementation of Mosaic and MixUp data augmentation in the training network that provided the feature extractor with 3× images of varying complexities and orientations and further aided in achieving higher detection accuracy. The proposed model is novel in terms of extracting rich features, performing augmentation at the training time and achieving high detection accuracy while maintaining the detection speed.