Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome-Reference-Cited by-同舟云学术

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome

Published:2018-11 Issue: Volume:37 Page:91-100
ISSN:2352-3964
Container-title:EBioMedicine
language:en
Short-container-title:EBioMedicine

Author:

Ko Bor-Sheng^ORCID,Wang Yu-Fen,Li Jeng-Lin,Li Chi-Cheng,Weng Pei-Fang,Hsu Szu-Chun,Hou Hsin-An,Huang Huai-Hsuan,Yao Ming,Lin Chien-Ting,Liu Jia-Hau,Tsai Cheng-Hong,Huang Tai-Chung,Wu Shang-Ju,Huang Shang-Yi,Chou Wen-Chien,Tien Hwei-Fang,Lee Chi-Chun^ORCID,Tang Jih-Luh

Funder

Ministry of Science and Technology

Publisher

Elsevier BV

Subject

General Biochemistry, Genetics and Molecular Biology,General Medicine

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4. Residual disease detected by multidimensional flow cytometry signifies high relapse risk in patients with de novo acute myeloid leukemia: a report from Children's Oncology Group;Loken;Blood,2012

5. Defining minimal residual disease in acute myeloid leukemia: Which platforms are ready for "prime time"?;Grimwade;Blood,2014

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