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Tao Wang

Ut Southwestern Medical Center

$1,651,809
Attributed
$3,188,483
Total exposure
5
Grants
5
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 $1.9M · FY201625
$2M$1.5M$1M$500K$0
'16
'17
'18
'19
'20
'21
'22
'23
'24
'25

Funding mix

By agency

NIH$3,188,483 · 5

By mechanism

R01$2,666,041 · 3
R41$360,442 · 1
R03$162,000 · 1

Top collaborators

Most similar at Ut Southwestern Medical Center

Same institution · by research overlap

Others in their field

Top investigators on “Predictive Modeling

Research focus

Predictive ModelingResearch PersonnelPerformanceDatabasesResourcesComplexDeep LearningImmunologyResponseCommunitiesScienceMethodologyMalignant NeoplasmsData SetBenchmarkingNeoantigensDeep Learning ModelAntigen BindingGenomicsAntigensImmune ResponseImmuneReportingT-Cell Receptor

Grant awards (11)

Machine learning for identifying antigen-antibody interactions from massive sequencing data$685,566
R01 · FY2025 · AI · contact PI
TCR-antigen foundation model to empower TCR-based diagnostics and therapeutics$528,157
R01 · FY2025 · AI · contact PI
Design and optimization of TCR-T therapeutics by an advanced T cell receptor-antigen binding prediction platform$360,442
R41 · FY2025 · CA · contact PI
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$313,223
R01 · FY2025 · CA · contact PI
Machine learning for identifying antigen-antibody interactions from massive sequencing data$8,200
R01 · FY2025 · AI · contact PI
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$385,855
R01 · FY2024 · CA · contact PI
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$6,465
R01 · FY2023 · CA · contact PI
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$359,678
R01 · FY2022 · CA · contact PI
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$378,897
R01 · FY2021 · CA · contact PI
Development of integrative models for early liver toxicity assessment$81,000
R03 · FY2017 · ES · contact PI
Development of integrative models for early liver toxicity assessment$81,000
R03 · FY2016 · ES · contact PI