Type
PreprintAuthors
Kulmanov, Maxat
Hoehndorf, Robert

KAUST Department
Bio-Ontology Research Group (BORG)Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant Number
URF/1/3454-01-01, URF/1/3790-01-01, FCC/1/1976-08-01.Date
2019-11-13Permanent link to this record
http://hdl.handle.net/10754/660714
Metadata
Show full item recordAbstract
Predicting 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.Citation
Kulmanov, M., & Hoehndorf, R. (2019). DeepPheno: Predicting single gene knockout phenotypes. doi:10.1101/839332Sponsors
We 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.
Publisher
Cold Spring Harbor LaboratoryDOI
10.1101/839332Additional Links
http://biorxiv.org/lookup/doi/10.1101/839332https://www.biorxiv.org/content/biorxiv/early/2019/11/13/839332.full.pdf
ae974a485f413a2113503eed53cd6c53
10.1101/839332
Scopus Count
Except where otherwise noted, this item's license is described as Archived with thanks to Cold Spring Harbor Laboratory