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David L Gibbs

Showing results (11-20 of 34) with videos related to

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Cancers|October 18, 2019
A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine LearningMaurizio Polano, Marco Chierici, Michele Dal Bo, et al.
Plos One|November 20, 2019
ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cellsSamuel A Danziger, David L Gibbs, Ilya Shmulevich, et al.
Journal of Clinical Bioinformatics|June 4, 2013
Protein co-expression network analysis (ProCoNA)David L Gibbs, Arie Baratt, Ralph S Baric, et al.
Journal of Clinical Microbiology|December 10, 2003
Comparison of the susceptibilities of Candida spp. to fluconazole and voriconazole in a 4-year global evaluation using disk diffusionKevin C Hazen, Ellen Jo Baron, Arnaldo Lopes Colombo, et al.
Cancers|February 13, 2025
Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBMDavid L Gibbs, Gino Cioffi, Boris Aguilar, et al.
Gigascience|July 23, 2020
A generalizable data-driven multicellular model of pancreatic ductal adenocarcinomaBoris Aguilar, David L Gibbs, David J Reiss, et al.
F1000Research|November 20, 2020
CRI iAtlas: an interactive portal for immuno-oncology researchJames A Eddy, Vésteinn Thorsson, Andrew E Lamb, et al.
JAMIA Open|June 7, 2021
PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trialNicholas O'Grady, David L Gibbs, Kawther Abdilleh, et al.
NPJ Digital Medicine|October 6, 2025
Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemiaYue Zhang, Jonathan Martinez, Bahar Tercan, et al.
Research Square|December 3, 2025
Analysis of clinical, single cell, and spatial data from the Human Tumor Atlas Network (HTAN) with massively distributed cloud-based queriesDavid L Gibbs, Dar'ya Pozhidayeva, Yamina Katariya, et al.
Pageof 4

Showing results (11-20 of 34) with videos related to

Sort By:
Pageof 4
Cancers|October 18, 2019
A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine LearningMaurizio Polano, Marco Chierici, Michele Dal Bo, et al.
Plos One|November 20, 2019
ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cellsSamuel A Danziger, David L Gibbs, Ilya Shmulevich, et al.
Journal of Clinical Bioinformatics|June 4, 2013
Protein co-expression network analysis (ProCoNA)David L Gibbs, Arie Baratt, Ralph S Baric, et al.
Journal of Clinical Microbiology|December 10, 2003
Comparison of the susceptibilities of Candida spp. to fluconazole and voriconazole in a 4-year global evaluation using disk diffusionKevin C Hazen, Ellen Jo Baron, Arnaldo Lopes Colombo, et al.
Cancers|February 13, 2025
Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBMDavid L Gibbs, Gino Cioffi, Boris Aguilar, et al.
Gigascience|July 23, 2020
A generalizable data-driven multicellular model of pancreatic ductal adenocarcinomaBoris Aguilar, David L Gibbs, David J Reiss, et al.
F1000Research|November 20, 2020
CRI iAtlas: an interactive portal for immuno-oncology researchJames A Eddy, Vésteinn Thorsson, Andrew E Lamb, et al.
JAMIA Open|June 7, 2021
PRoBE the cloud toolkit: finding the best biomarkers of drug response within a breast cancer clinical trialNicholas O'Grady, David L Gibbs, Kawther Abdilleh, et al.
NPJ Digital Medicine|October 6, 2025
Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemiaYue Zhang, Jonathan Martinez, Bahar Tercan, et al.
Research Square|December 3, 2025
Analysis of clinical, single cell, and spatial data from the Human Tumor Atlas Network (HTAN) with massively distributed cloud-based queriesDavid L Gibbs, Dar'ya Pozhidayeva, Yamina Katariya, et al.
Pageof 4