A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei.

Phillip, Jude M; Han, Kyu-Sang; Chen, Wei-Chiang; Wirtz, Denis; Wu, Pei-Hsun

Phillip, Jude M; Han, Kyu-Sang; Chen, Wei-Chiang; Wirtz, Denis; Wu, Pei-Hsun.

Afiliação

Phillip JM; Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA.
Han KS; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Chen WC; Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA.
Wirtz D; Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA.
Wu PH; Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA. wirtz@jhu.edu.

Nat Protoc ; 16(2): 754-774, 2021 02.

Article em En | MEDLINE | ID: mdl-33424024

Assuntos

Forma Celular/fisiologia; Imageamento Tridimensional/métodos; Microscopia Confocal/métodos; Algoritmos; Núcleo Celular/fisiologia; Humanos; Software; Aprendizado de Máquina não Supervisionado/estatística & dados numéricos

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Microscopia Confocal / Imageamento Tridimensional / Forma Celular Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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