KAUST DepartmentBio-Ontology Research Group (BORG)
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberURF/1/3454-01-01, URF/1/3790-01-01, FCC/1/1976-08-01.
Permanent link to this recordhttp://hdl.handle.net/10754/660714
MetadataShow full item record
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.
CitationKulmanov, M., & Hoehndorf, R. (2019). DeepPheno: Predicting single gene knockout phenotypes. doi:10.1101/839332
SponsorsWe acknowledge the use of computational resources from the KAUST Supercomputing Core Laboratory.
This 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.
PublisherCold Spring Harbor Laboratory
Except where otherwise noted, this item's license is described as Archived with thanks to Cold Spring Harbor Laboratory