flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry.

about

flowLearn: Fast and precise identification and quality checking of cell populations in flow cytometry.

Item

http://www.wikidata.org/entity/Q50026670

description

2018 nî lūn-bûn

@nan

2018年の論文

@ja

2018年学术文章

@wuu

2018年学术文章

@zh

2018年学术文章

@zh-cn

2018年学术文章

@zh-hans

2018年学术文章

@zh-my

2018年学术文章

@zh-sg

2018年學術文章

@yue

2018年學術文章

@zh-hant

name

flowLearn: Fast and precise id ...... populations in flow cytometry.

@en

flowLearn: Fast and precise id ...... populations in flow cytometry.

@nl

type

Item

label

flowLearn: Fast and precise id ...... populations in flow cytometry.

@en

flowLearn: Fast and precise id ...... populations in flow cytometry.

@nl

prefLabel

flowLearn: Fast and precise id ...... populations in flow cytometry.

@en

flowLearn: Fast and precise id ...... populations in flow cytometry.

@nl

P1476

flowLearn: fast and precise id ...... populations in flow cytometry

@en

P2093

Adam Laing

http://www.w3.org/2001/XMLSchema#string

Cedric Chauve

http://www.w3.org/2001/XMLSchema#string

Lucie Abeler-Dörner

http://www.w3.org/2001/XMLSchema#string

Markus Lux

http://www.w3.org/2001/XMLSchema#string

P2860

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium

Critical assessment of automated flow cytometry data analysis techniques.

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia.

flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.

FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.

The end of gating? An introduction to automated analysis of high dimensional cytometry data.

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.

Per-channel basis normalization methods for flow cytometry data.

The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping

P304

2245-2253

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.1093/BIOINFORMATICS/BTY082

http://www.w3.org/2001/XMLSchema#string

P407

English

P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Ryan R Brinkman

Anna Lorenc

Barbara Hammer

P577

2018-07-01T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

29462241

http://www.w3.org/2001/XMLSchema#string

P932

6022609

http://www.w3.org/2001/XMLSchema#string