Toggle navigation
Home
Search
Services
Blog
Contact
About
Minority Predoctoral Fellowship Program - NIGMS
Eke, Agatha N.
Johns Hopkins University, Baltimore, MD, United States
Search 3 grants from Agatha Eke
Search grants from Johns Hopkins University
Share this grant:
:
:
Abstract
Funding
Institution
Related projects
Publications
Comments
Recent in Grantomics:
Ohio State University
vs. funders. Who wins?
Read more...
How should you pick the next fundable research topic?
Read more...
Recently viewed grants:
Regulation Mechanisms of Thyroid Hormone Receptors in the Heart
Human Cathepsin G: Expression, C-Terminal Processing and Dual Specificity
Surveillance of HIV/AIDS Related Events Among Persons Not Receiving Care in NJ
Cell based biosensor fabricated by soft Lithography
SIV & HIV Trimeric Envelope Protein
Recently added grants:
SUMO-regulation of ion channels via PIP2
Endogenous Hydrogen Sulfide Enzymes in Heart Failure
Modeling Social and Non-Social Learning in Autism
Targeting ACE2 ubiquitination for hypertension
Highly Multiplexed Cell-Based GPCR Assay.
Abstract
Funding Agency
Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Predoctoral Individual National Research Service Award (F31)
Project #
1F31GM016448-01A1
Application #
2170986
Study Section
Special Emphasis Panel (SRC)
Project Start
1995-03-29
Project End
Budget Start
1994-10-01
Budget End
1995-09-30
Support Year
1
Fiscal Year
1994
Total Cost
Indirect Cost
Institution
Name
Johns Hopkins University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
045911138
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Related projects
NIH 1996
F31 GM
Minority Predoctoral Fellowship Program - NIGMS
Eke, Agatha N. / Johns Hopkins University
NIH 1995
F31 GM
Minority Predoctoral Fellowship Program - NIGMS
Eke, Agatha N. / Johns Hopkins University
NIH 1994
F31 GM
Minority Predoctoral Fellowship Program - NIGMS
Eke, Agatha N. / Johns Hopkins University
Publications
Beck, Daniel; Foster, James A
(2015)
Machine learning classifiers provide insight into the relationship between microbial communities and bacterial vaginosis.
BioData Min 8:23
Comments
Be the first to comment on this grant