The genome-wide association study(GWAS)aims to detect associations between individual single nucleotide polymorphisms(SNPs)or SNP interactions and phenotypes to decipher the genetic mechanism.Existing GWAS analysis to...The genome-wide association study(GWAS)aims to detect associations between individual single nucleotide polymorphisms(SNPs)or SNP interactions and phenotypes to decipher the genetic mechanism.Existing GWAS analysis tools have different focuses and advantages,but suffer a series of tedious and heterogeneous configurations for computation.It is inconvenient for researchers to simply choose and apply these tools,statistically and biologically analyze their results for different usages.To address these issues,we develop a user friendly web pipeline GWASTool for detecting associations,which includes simulation data generation,associated loci detection,result visualization,analysis and comparison.GWASTool provides a unified and plugin-able framework to encapsulate the heterogeneity of GWAS algorithms,simplifies the analysis steps and energizes GWAS tasks.GWASTool is implemented in Java and is freely available for public use at http://wwW.sdu-idea.cn/GWASTool.The website hosts a comprehensive collection of resources,including a user manual,description of integrated algorithms,data examples and standalone version for download.展开更多
Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares a...Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.展开更多
基金the National Natural Science Foundation of China(62031003 and 62072380)Shandong Provincial Key Research and Development Program(2021CXGC010506).
文摘The genome-wide association study(GWAS)aims to detect associations between individual single nucleotide polymorphisms(SNPs)or SNP interactions and phenotypes to decipher the genetic mechanism.Existing GWAS analysis tools have different focuses and advantages,but suffer a series of tedious and heterogeneous configurations for computation.It is inconvenient for researchers to simply choose and apply these tools,statistically and biologically analyze their results for different usages.To address these issues,we develop a user friendly web pipeline GWASTool for detecting associations,which includes simulation data generation,associated loci detection,result visualization,analysis and comparison.GWASTool provides a unified and plugin-able framework to encapsulate the heterogeneity of GWAS algorithms,simplifies the analysis steps and energizes GWAS tasks.GWASTool is implemented in Java and is freely available for public use at http://wwW.sdu-idea.cn/GWASTool.The website hosts a comprehensive collection of resources,including a user manual,description of integrated algorithms,data examples and standalone version for download.
文摘Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.
