Výsledky vyhledávání

Akademický článek

Investigating the relationship between radiographic joint space width loss and deep learning-derived magnetic resonance imaging-based cartilage thickness loss in the medial weight-bearing region of the tibiofemoral joint

Autor: Mary Catherine C. Minnig, Liubov Arbeeva, Marc Niethammer, Daniel Nissman, Jennifer L. Lund, J.S. Marron, Yvonne M. Golightly, Amanda E. Nelson

Publikováno v: Osteoarthritis and Cartilage Open, Vol 6, Iss 3, Pp 100508- (2024)

Objective: To investigate the relationship between measures of radiographic joint space width (JSW) loss and magnetic resonance imaging (MRI)-based cartilage thickness loss in the medial weight-bearing region of the tibiofemoral joint over 12–24 mo

Externí odkaz: https://doaj.org/article/0d27292c397c49bc90c92c0e6becf3f3

Zobrazit plný text záznamu

Akademický článek

Patterns of variation among baseline femoral and tibial cartilage thickness and clinical features: Data from the osteoarthritis initiative

Autor: Thomas H. Keefe, Mary Catherine Minnig, Liubov Arbeeva, Marc Niethammer, Zhenlin Xu, Zhengyang Shen, Boqi Chen, Daniel B. Nissman, Yvonne M. Golightly, J.S. Marron, Amanda E. Nelson

Publikováno v: Osteoarthritis and Cartilage Open, Vol 5, Iss 1, Pp 100334- (2023)

Objective: To employ novel methodologies to identify phenotypes in knee OA based on variation among three baseline data blocks: 1) femoral cartilage thickness, 2) tibial cartilage thickness, and 3) participant characteristics and clinical features. M

Externí odkaz: https://doaj.org/article/28540bb0e10f444894f3747fbb30b110

Zobrazit plný text záznamu

Akademický článek

Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Abstract P3-09-08: An automated tool to determine optimal cluster numbers in single-cell RNA sequencing data identifies key prognostic subsets of T cells in breast tumors

Autor: Siyao Liu, Aatish Thennavan, J.S. Marron, Charles Perou

Publikováno v: Cancer Research. 82:P3-09

Background: Single-cell RNA sequencing (scRNAseq) enables gene expression profiling of individual cells, thus providing new opportunities to identify and characterize cell types and tumor cell states, and which is typically accomplished through some

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::858c5428151542dca37b23d33185f19f
https://doi.org/10.1158/1538-7445.sabcs21-p3-09-08

Zobrazit plný text záznamu

The Poisson distribution model fits UMI-based single-cell RNA-sequencing data

Autor: Yue Pan, Justin T. Landis, Razia Moorad, Di Wu, J.S. Marron, Dirk P. Dittmer

Background: Modeling of single cell RNA-sequencing (scRNA-seq) data remains challenging due to a high percentage of zeros and data heterogeneity, so improved modeling has strong potential to benefit many downstream data analyses. The existing zero-in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::54a591af53e92b42636f20f7397059b8
https://doi.org/10.21203/rs.3.rs-2517698/v1

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání