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dc.contributor.authorKulmanov, Maxat
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2019-12-22T06:00:44Z
dc.date.available2019-12-22T06:00:44Z
dc.date.issued2019-11-13
dc.identifier.citationKulmanov, M., & Hoehndorf, R. (2019). DeepPheno: Predicting single gene knockout phenotypes. doi:10.1101/839332
dc.identifier.doi10.1101/839332
dc.identifier.urihttp://hdl.handle.net/10754/660714
dc.description.abstractPredicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations.
dc.description.sponsorshipWe acknowledge the use of computational resources from the KAUST Supercomputing Core Laboratory.
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, URF/1/3790-01-01, FCC/1/1976-08-01.
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttp://biorxiv.org/lookup/doi/10.1101/839332
dc.relation.urlhttps://www.biorxiv.org/content/biorxiv/early/2019/11/13/839332.full.pdf
dc.rightsArchived with thanks to Cold Spring Harbor Laboratory
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDeepPheno: Predicting single gene knockout phenotypes
dc.typePreprint
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
kaust.personKulmanov, Maxat
kaust.personHoehndorf, Robert
kaust.grant.numberURF/1/3454-01-01, URF/1/3790-01-01, FCC/1/1976-08-01.
refterms.dateFOA2019-12-22T06:01:19Z
kaust.acknowledged.supportUnitSupercomputing Core Laboratory


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Archived with thanks to Cold Spring Harbor Laboratory
Except where otherwise noted, this item's license is described as Archived with thanks to Cold Spring Harbor Laboratory