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High-precision multiclass cell classification by supervised machine learning on lectin microarray data
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DOI：10.1016/j.reth.2020.09.005.png)
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Abstract
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Introduction: Establishment of a cell classification platform for evaluation and selection of human pluripotent stem cells (hPSCs) is of great importance to assure the efficacy and safety of cell-based therapy. In our previous work, we introduced a discriminant function that evaluates pluripotency from the cells' glycome. However, it is not yet suitable for general use. Methods: The current study aims to establish a high-precision cell classification platform introducing supervised machine learning and test the platform on glycome analysis as a proof-of-concept study. We employed linear classification and neural network to the lectin microarray data from 1577 human cells and categorized them into five classes including hPSCs. Results: The linear-classification-based model and the neural-network-based model successfully predicted the sample type with accuracies of 89% and 97%, respectively. Conclusions: Because of the high recognition accuracies and the small amount of computing resources required for these analyses, our platform can be a high precision conventional cell classification system for hPSCs. (C) 2020, The Japanese Society for Regenerative Medicine. Production and hosting by Elsevier B.V.
Keywords:
Lectin microarray
Linear classification
Neural network
Artificial intelligence
Pluripotent stem cells
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