Cargando…
Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays
Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still limited due to technical artifacts, tissue loss...
Autores principales: | Ternes, Luke, Lin, Jia-Ren, Chen, Yu-An, Gray, Joe W., Chang, Young Hwan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9555662/ https://www.ncbi.nlm.nih.gov/pubmed/36178966 http://dx.doi.org/10.1371/journal.pcbi.1010505 |
Ejemplares similares
-
3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding
por: Burlingame, Erik, et al.
Publicado: (2023) -
VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts
por: Ternes, Luke, et al.
Publicado: (2020) -
RapidMic: Rapid Computation of the Maximal Information Coefficient
por: Tang, Dongming, et al.
Publicado: (2014) -
Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms
por: Burlingame, Erik A., et al.
Publicado: (2021) -
DNA analysis with multiplex microarray-enhanced PCR
por: Pemov, A., et al.
Publicado: (2005)