Code for the paper "cytoGPNet: Enhancing Clinical Outcome Prediction Accuracy Using Longitudinal Cytometry Data in Small Cohort Studies"
Contributors
Annotator:
Contact person:
Data curator:
Researchers:
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1.
Duke University
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2.
Duke University School of Medicine
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3.
University of California, Santa Barbara
Description
The software package provides an implementation of cytoGPNet, as described in the paper "cytoGPNet: Enhancing Clinical Outcome Prediction Accuracy Using Longitudinal Cytometry Data in Small Cohort Studies".
Release is on Github here.
Other
Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA provided financial support for TOP1501 study. The opinions expressed in this paper are those of the authors and do not necessarily represent those of Merck Sharp & Dohme LLC. This research was also supported by the Duke University Center for AIDS Research (CFAR), an NIH funded program (5P30 AI064518) and NIH P01 (2 P01 AI129859). The authors gratefully recognize the contributions of Jennifer Enzor and Prekshaben Patel, who generated all of the original TOP1501 flow cytometry data in the Duke Immune Profiling Core (DIPC), a designated Shared Resource of the NIH-sponsored Duke Cancer Institute (5P30-CA014236-50).
Files
cytoGPNet.zip
Files
(49.3 kB)
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md5:81890478b392fd5b5968c7c79c756a98
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Additional details
Software
- Programming language
- Python