Deepsvp: Integration of Genotype and Phenotype for Structural Variant Prioritization Using Deep Learning
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.28.428557; this version posted April 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. i i “output” — 2021/4/9 — 6:14 — page 1 — #1 i i Bioinformatics doi.10.1093/bioinformatics/xxxxxx Applications Note Genome analysis DeepSVP: Integration of genotype and phenotype for structural variant prioritization using deep learning Azza Althagafi 1,2, Lamia Alsubaie 3, Nagarajan Kathiresan 4, Katsuhiko Mineta 1, Taghrid Aloraini 3, Fuad Almutairi 5,6, Majid Alfadhel 5,6,7, Takashi Gojobori 8, Ahmad Alfares 3,7,9, and Robert Hoehndorf1,∗ 1 Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia (SA); 2 Computer Science Department, College of Computers and Information Technology, Taif University, Taif, SA; 3 Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City (KAMC), Riyadh, SA; 4 Supercomputing Core Lab, KAUST, Thuwal, SA; 5 Division of Genetics, Department of Pediatrics, KAMC, Riyadh, SA; 6 King Saud bin Abdulaziz University for Health Sciences, KAMC, Riyadh, SA; 7 King Abdullah International Medical Research Center, Riyadh, SA; 8 Biological and Environmental Science and Engineering Division, CBRC, KAUST, Thuwal, SA; 9 Department of Pediatrics, College of Medicine, Qassim University, Qassim, SA. ∗To whom correspondence should be addressed. Associate Editor: XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX Abstract Motivation: Structural genomic variants account for much of human variability and are involved in several diseases.
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