← Leaderboards
Xinlei Wang
Southern Methodist University
$2,791,563
Attributed
$3,513,622
Total exposure
4
Grants
3
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 $1.6M · FY2015–25$2M$1.5M$1M$500K$0
'15
'16
'17
'18
'19
'20
'21
'22
'23
'24
'25
Funding mix
By agency
NIH$3,513,622 · 4
By mechanism
R01$2,725,942 · 2
R15$787,680 · 2
Top collaborators
- Tao Wang5 shared
Most similar at Southern Methodist University
Same institution · by research overlap
- Barry Lee$556,482
- Raanju Sundararajan$177,718
Others in their field
Top investigators on “Statistics”
- David Heimbrook · Leidos Biomedical Research, Inc.$1,161,798,033
- Lawrence Corey · Fred Hutchinson Cancer Center$718,717,077
- Sonia M Thomas · Research Triangle Institute$701,865,642
- Leonard Freedman · Leidos Biomedical Research, Inc.$615,067,739
- Tracy L Nolen · Research Triangle Institute$474,487,152
- Jeffrey P Krischer · University Of South Florida$427,267,393
Research focus
StatisticsResearch PersonnelData SetSourceComplexPerformanceLearningClinical PracticeAnalysis PipelineMalignant NeoplasmsTranscriptome SequencingCommunitiesBenchmarkingResourcesReproducibilityGenomicsPublicationsRegulationClinical DataBiological ProcessPlayPredicting ResponseNeglectMethodology
Grant awards (8)
Statistical and Deep Generative Modeling for Enhanced CyTOF Data Interpretation and Discovery$1,281,824
R01 · FY2025 · GM · contact PI
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$313,223
R01 · FY2025 · CA
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$385,855
R01 · FY2024 · CA
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$6,465
R01 · FY2023 · CA
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$359,678
R01 · FY2022 · CA
Applying deep learning to predict T cell receptor binding specificity of neoantigens and response to checkpoint inhibitors$378,897
R01 · FY2021 · CA
Normalizing Gene Expression Data from Formalin-Fixed Paraffin-Embedded Samples$431,586
R15 · FY2019 · GM · contact PI
Wisdom of Crowds: Integrating Gene Set Enrichment Studies Involving RNA-Seq.$356,094
R15 · FY2015 · GM · contact PI