Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- ADHD (1)
- Applied computing (1)
- Bio-medical science (1)
- Bioinformatics (1)
- Biology computing (1)
-
- Chromosome structures (1)
- Computational biology (1)
- Computer science (1)
- DNA (1)
- Data analysis (1)
- Gel bead in emulsion microfluidic method (1)
- Genetics (1)
- Genome (1)
- Genome gap (1)
- Genome-wide assembly (1)
- Genomic regions (1)
- Genomics (1)
- Human (1)
- Human reference sequence (1)
- Human subtelomere regions (1)
- Humans (1)
- Knowledge engineering (1)
- Microfluidics (1)
- Misassembly (1)
- Neuroimaging (1)
- Pipelines (1)
- QUAST (1)
- Quality metric (1)
- REXTAL misassemblies (1)
- Reference-based assessment module (1)
Articles 1 - 2 of 2
Full-Text Articles in Life Sciences
Analysis Of Subtelomeric Rextal Assemblies Using Quast, Tunazzina Islam, Desh Ranjan, Mohammad Zubair, Eleanor Young, Ming Xiao, Harold Riethman
Analysis Of Subtelomeric Rextal Assemblies Using Quast, Tunazzina Islam, Desh Ranjan, Mohammad Zubair, Eleanor Young, Ming Xiao, Harold Riethman
Computer Science Faculty Publications
Genomic regions of high segmental duplication content and/or structural variation have led to gaps and misassemblies in the human reference sequence, and are refractory to assembly from whole-genome short-read datasets. Human subtelomere regions are highly enriched in both segmental duplication content and structural variations, and as a consequence are both impossible to assemble accurately and highly variable from individual to individual. Recently, we developed a pipeline for improved region-specific assembly called Regional Extension of Assemblies Using Linked-Reads (REXTAL). In this study, we evaluate REXTAL and genome-wide assembly (Supernova) approaches on 10X Genomics linked-reads data sets partitioned and barcoded using the …
Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna
Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna
Computer Science Faculty Publications
Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …