A machine learning model that incorporates CD45 mean fluorescence intensity (MFI) and cell composition predicts poor viability of hematopoietic progenitor cells after freeze-thaw Meeting Abstract
| Authors: | Al-Riyami, A. Z.; Kadauke, S.; Hanna, R. S.; Azar, A. P.; Maryamchik, E.; Zheng, X.; Zhang, X.; Finn, C.; Giacobbe, N.; Rieser, R.; Tahrir, F. G.; Machietto, R.; Choudhari, S.; Wang, Y. |
| Abstract Title: | A machine learning model that incorporates CD45 mean fluorescence intensity (MFI) and cell composition predicts poor viability of hematopoietic progenitor cells after freeze-thaw |
| Meeting Title: | 28th Annual Scientific Meeting of the International Society for Cell and Gene Therapy (ISCT) |
| Keywords: | hematopoietic progenitor cells; viability; machine learning model |
| Journal Title: | Cytotherapy |
| Volume: | 24 |
| Issue: | 5 Suppl. |
| Meeting Dates: | 2022 May 4-7 |
| Meeting Location: | San Francisco, CA |
| ISSN: | 1465-3249 |
| Publisher: | Elsevier Science Ltd. |
| Date Published: | 2022-05-01 |
| Start Page: | S99 |
| End Page: | S100 |
| Language: | English |
| ACCESSION: | WOS:000796192800165 |
| PROVIDER: | wos |
| DOI: | 10.1016/S1465-3249(22)00284-5 |
| Notes: | Meeting Abstract: 403 -- In section: "Poster abstracts: Hematopoietic Stem/Progenitor Cells and Engineering -- Source: Wos |
