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Ilan Wapinski

Showing results (21-30 of 32) with videos related to

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Cell Reports. Medicine|April 19, 2023
Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASHJake Conway, Maryam Pouryahya, Yevgeniy Gindin, et al.
BMC Genomics|March 29, 2012
Comparative analysis of Mycobacterium and related Actinomycetes yields insight into the evolution of Mycobacterium tuberculosis pathogenesisAbigail Manson McGuire, Brian Weiner, Sang Tae Park, et al.
Hepatology (Baltimore, Md.)|August 1, 2021
A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH CirrhosisJaime Bosch, Chuhan Chung, Oscar M Carrasco-Zevallos, et al.
Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer|August 4, 2025
Digital Versus Manual PD-L1 Scoring in Advanced NSCLC From the IMpower110 and IMpower150 TrialsRoy S Herbst, Hen Prizant, Daniel Ruderman, et al.
NPJ Precision Oncology|June 19, 2024
AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosisJohn Abel, Suyog Jain, Deepta Rajan, et al.
Medrxiv : the Preprint Server for Health Sciences|May 10, 2023
AI-based histologic scoring enables automated and reproducible assessment of enrollment criteria and endpoints in NASH clinical trialsJanani S Iyer, Harsha Pokkalla, Charles Biddle-Snead, et al.
Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer|December 9, 2023
Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 StudySanja Dacic, William D Travis, Jennifer M Giltnane, et al.
Hepatology (Baltimore, Md.)|February 11, 2021
A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASHAmaro Taylor-Weiner, Harsha Pokkalla, Ling Han, et al.
Nature Medicine|August 7, 2024
AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseasesJanani S Iyer, Dinkar Juyal, Quang Le, et al.
Hepatology (Baltimore, Md.)|November 10, 2020
Combination Therapies Including Cilofexor and Firsocostat for Bridging Fibrosis and Cirrhosis Attributable to NASHRohit Loomba, Mazen Noureddin, Kris V Kowdley, et al.
Pageof 4

Showing results (21-30 of 32) with videos related to

Sort By:
Pageof 4
Cell Reports. Medicine|April 19, 2023
Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASHJake Conway, Maryam Pouryahya, Yevgeniy Gindin, et al.
BMC Genomics|March 29, 2012
Comparative analysis of Mycobacterium and related Actinomycetes yields insight into the evolution of Mycobacterium tuberculosis pathogenesisAbigail Manson McGuire, Brian Weiner, Sang Tae Park, et al.
Hepatology (Baltimore, Md.)|August 1, 2021
A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH CirrhosisJaime Bosch, Chuhan Chung, Oscar M Carrasco-Zevallos, et al.
Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer|August 4, 2025
Digital Versus Manual PD-L1 Scoring in Advanced NSCLC From the IMpower110 and IMpower150 TrialsRoy S Herbst, Hen Prizant, Daniel Ruderman, et al.
NPJ Precision Oncology|June 19, 2024
AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosisJohn Abel, Suyog Jain, Deepta Rajan, et al.
Medrxiv : the Preprint Server for Health Sciences|May 10, 2023
AI-based histologic scoring enables automated and reproducible assessment of enrollment criteria and endpoints in NASH clinical trialsJanani S Iyer, Harsha Pokkalla, Charles Biddle-Snead, et al.
Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer|December 9, 2023
Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 StudySanja Dacic, William D Travis, Jennifer M Giltnane, et al.
Hepatology (Baltimore, Md.)|February 11, 2021
A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASHAmaro Taylor-Weiner, Harsha Pokkalla, Ling Han, et al.
Nature Medicine|August 7, 2024
AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseasesJanani S Iyer, Dinkar Juyal, Quang Le, et al.
Hepatology (Baltimore, Md.)|November 10, 2020
Combination Therapies Including Cilofexor and Firsocostat for Bridging Fibrosis and Cirrhosis Attributable to NASHRohit Loomba, Mazen Noureddin, Kris V Kowdley, et al.
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