Jung Hun Oh

Sloan-Kettering Inst Can Research

$638,603

Attributed

$1,684,912

Total exposure

Grants

Lead (contact PI)

Attributed= this PI's even-split share of every grant they're on (the fair, additive number). Exposure = full size of all those grants.

Funding over time

peak $633.7K · FY2020–25

$1M$750K$500K$250K$0

'20

'21

'22

'23

'24

'25

Funding mix

By agency

NIH$1,684,912 · 2

By mechanism

R01$1,223,116 · 1

R21$461,796 · 1

Top collaborators

Most similar at Sloan-Kettering Inst Can Research

Same institution · by research overlap

Others in their field

Other Emerging Leaders on “Machine Learning”

Klaus Romero · Critical Path Institute$29,190,596
Evan Z Macosko · Broad Institute, Inc.$27,107,011
Maria C Carrillo · Indiana University Indianapolis$19,445,642
Leslie M Schoop · The University Corporation, Northridge$18,180,416
Rene S. Kahn · Icahn School Of Medicine At Mount Sinai$16,767,001
John E West · University Of Texas At Austin$15,799,827

Research focus

Machine LearningBioinformaticsRadiationPathway InteractionsSingle Nucleotide PolymorphismValidationCohortDoseHigh RiskMachine Learning MethodRadiation TherapySamplingSubgroupToxic EffectBiologicalClinical Decision-MakingData SetDesignGenetic RiskGenome Wide Association StudyInnovationGeneticBreast Cancer SurvivorBreast Cancer Risk Factor

Grant awards (4)

Multi-cohort validation of machine learning radiogenomic models (ML-RGx) to predict late toxicity in prostate cancer$633,691

R01 · FY2025 · CA

Multi-cohort validation of machine learning radiogenomic models (ML-RGx) to predict late toxicity in prostate cancer$589,425

R01 · FY2024 · CA

Radiotherapy-associated breast cancer: machine learning on genotypes to predict individualized risk$167,926

R21 · FY2021 · CA · contact PI

Radiotherapy-associated breast cancer: machine learning on genotypes to predict individualized risk$293,870

R21 · FY2020 · CA · contact PI