.png){kind=icon}
.png){kind=icon}
Return
MAPS: pathologist-level cell type annotation from tissue images through machine learning
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-023-44188-w.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension. Current cell annotation methods using high-plex spatial proteomics data are resource intensive and demand iterative expert input. Here, the authors present MAPS (Machine learning for Analysis of Proteomics in Spatial biology), an approach that facilitates rapid and precise cell type identification with human-level accuracy from spatial proteomics data.
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
15.7
Papers:
9.3W
Citations:
91.2W
Organization
View more organizations