Detalhe da pesquisa
1.
[Artificial intelligence in ophthalmology : Guidelines for physicians for the critical evaluation of studies]. / Künstliche Intelligenz in der Augenheilkunde : Leitfaden für Ärzte zur kritischen Bewertung von Studien.
Ophthalmologe
; 117(10): 973-988, 2020 Oct.
Artigo
em Alemão
| MEDLINE | ID: mdl-32857270
2.
Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization.
Commun Biol
; 2: 229, 2019.
Artigo
em Inglês
| MEDLINE | ID: mdl-31240267
3.
QFMatch: multidimensional flow and mass cytometry samples alignment.
Sci Rep
; 8(1): 3291, 2018 02 19.
Artigo
em Inglês
| MEDLINE | ID: mdl-29459702
4.
Earth Mover's Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations.
PLoS One
; 11(3): e0151859, 2016.
Artigo
em Inglês
| MEDLINE | ID: mdl-27008164
5.
Peer Assessment Enhances Student Learning: The Results of a Matched Randomized Crossover Experiment in a College Statistics Class.
PLoS One
; 10(12): e0143177, 2015.
Artigo
em Inglês
| MEDLINE | ID: mdl-26683053
6.
AutoGate: automating analysis of flow cytometry data.
Immunol Res
; 58(2-3): 218-23, 2014 May.
Artigo
em Inglês
| MEDLINE | ID: mdl-24825775
7.
Science not art: statistically sound methods for identifying subsets in multi-dimensional flow and mass cytometry data sets.
Nat Rev Immunol
; 18(1): 77, 2017 12 22.
Artigo
em Inglês
| MEDLINE | ID: mdl-29269766
8.
Automatic clustering of flow cytometry data with density-based merging.
Adv Bioinformatics
; : 686759, 2009.
Artigo
em Inglês
| MEDLINE | ID: mdl-20069107