Due to continuous increase in the number of malware (according to AV-Test institute total ∼ 8 × 10 8 malware are already known, and every day they register ∼ 2.5 × 10 4 malware) and files in the computational devices, it is very important to design a system which not only effectively but can also efficiently detect the new or previously unseen malware to prevent/minimize the damages. Therefore, this paper presents a novel group-wise approach for the efficient detection of malware by parallelizing the classification using the power of GPGPU and shown that by using the Naive Bayes classifier the detection speed-up can be boosted up to 200x. The investigation also shows that the classification time increases significantly with the number of features.
Background: Neurological diseases are phenotypically and genotypically heterogeneous. Clinical exome sequencing (CES) has been shown to provide a high diagnostic yield for these disorders in the European population but remains to be demonstrated for the Indian population.
Methods: A cohort of 19 idiopathic patients with neurological phenotypes, primarily intellectual disability and developmental delay, were recruited. CES covering 4620 genes was performed on all patients. Candidate variants were validated by Sanger sequencing.
Results: CES in 19 patients provided identified 21 variants across 16 genes which have been associated with different neurological disorders. Fifteen variants were reported previously and 6 variants were novel to our study. Eleven patients were diagnosed with autosomal dominant de novo variants, 7 with autosomal recessive and 1 with X-linked recessive variants. CES provided definitive diagnosis to 10 patients, hence the diagnostic yield was 53%.
Conclusion: Our study suggests that the diagnostic yield of CES in the Indian population is comparable to that reported in the European population. CES together with deep phenotyping could be a cost-effective way of diagnosing rare neurological disorders in the Indian population.
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