← Leaderboards
Dmitry Korkin
Worcester Polytechnic Institute
$999,386
Attributed
$1,645,371
Total exposure
2
Grants
2
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 $326.7K · FY2018–25$500K$375K$250K$125K$0
'18
'19
'20
'21
'22
'23
'24
'25
Funding mix
By agency
NIH$1,645,371 · 2
By mechanism
R01$1,291,970 · 1
R21$353,401 · 1
Top collaborators
- Gloria Sheynkman4 shared
Others in their field
Other Emerging Leaders on “Molecular”
- Leonard Freedman · Leidos Biomedical Research, Inc.$468,573,385
- Lynn Briscoe · Leidos Biomedical Research, Inc.$137,360,557
- Ethan Dmitrovsky · Leidos Biomedical Research, Inc.$109,548,439
- Leonard Freedmand · Leidos Biomedical Research, Inc.$85,594,314
- Peter W Pisters · University Of Tx Md Anderson Can Ctr$76,895,673
- Scott Hensley · University Of Pennsylvania$41,662,743
Research focus
MolecularComputing MethodologiesProtein Protein InteractionTranscriptomicsProteinsStructureLinkProtein IsoformsCell PhysiologyVariantGeneticData SetGenesGene FunctionAlternative SplicingHuman GenomeConsumptionBindingCellsDetection MethodBenchmarkingCase StudyExperimental StudyCancer Cell Line
Grant awards (6)
Predicting the functional impact of alternative splicing on protein-protein interactions using an integrated approach$321,403
R01 · FY2025 · LM · contact PI
Predicting the functional impact of alternative splicing on protein-protein interactions using an integrated approach$321,749
R01 · FY2024 · LM · contact PI
Predicting the functional impact of alternative splicing on protein-protein interactions using an integrated approach$322,094
R01 · FY2023 · LM · contact PI
Predicting the functional impact of alternative splicing on protein-protein interactions using an integrated approach$326,724
R01 · FY2022 · LM · contact PI
Functional characterization of genetic and post-transcriptional variation using machine learning methods$193,732
R21 · FY2019 · LM · contact PI
Functional characterization of genetic and post-transcriptional variation using machine learning methods$159,669
R21 · FY2018 · LM · contact PI