Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis (Updated November 17, 2023).
In: Pain & Central Nervous System Week, 2023-12-04, S. 1194-1194
serialPeriodical
Zugriff:
A preprint abstract from biorxiv.org discusses the development of a machine learning tool called SIMS (Scalable, Interpretable Machine Learning for Single-Cell) for classifying single-cell data. The tool has been benchmarked against other label transfer tools and has shown high accuracy in classifying cells in complex tissues such as the brain. It has also been successful in predicting neuronal subtypes in the developing brain and identifying genetic variations in cell lines derived from different pluripotent stem cell lines. The tool is versatile and robust, making it a valuable resource for cell-type classification in single-cell datasets. However, it is important to note that this preprint has not yet undergone peer review. [Extracted from the article]
Copyright of Pain & Central Nervous System Week is the property of NewsRx and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
Unraveling Neuronal Identities Using SIMS: A Deep Learning Label Transfer Tool for Single-Cell RNA Sequencing Analysis (Updated November 17, 2023).
|
---|---|
Zeitschrift: | Pain & Central Nervous System Week, 2023-12-04, S. 1194-1194 |
Veröffentlichung: | 2023 |
Medientyp: | serialPeriodical |
ISSN: | 1531-6394 (print) |
Sonstiges: |
|