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Machine Learning Approaches for Stem Cells

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Abstract

Purpose of Review

Machine learning (ML) enables high-throughput analysis of multimodal data generated from stem cell experiments such as gene expression data, images of cells, or proteomic data. In this review, we analyse the progression of ML adaptation in advancing the field of stem cell research.

Recent Findings

On the one hand, the field of stem cell phenotypic characterisation is experiencing a significant growth, largely due to the successful implementation of deep networks in domains with similar problem characteristics (i.e., rapid advances of the image recognition field). On the other hand, genotypic characterisation is gradually gaining traction as researchers are beginning to apply ML to understand the genetic and molecular mechanisms behind stem cell behaviour.

Summary

The use of advanced machine learning techniques, such as deep networks, is demonstrating promising results in phenotypic stem cell characterisation, although it is still lagging slightly in genotypic characterisation. Despite this progress, significant challenges persist, including ensuring the interpretability of ML models, limited availability of annotated datasets, improving the accuracy and quality of training data, and navigating ethical considerations.

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Authors and Affiliations

  1. Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Kampus Alam UniMAP, Pauh Putra, 02600, Arau, Perlis, Malaysia

    Mazlee Mazalan

  2. Faculty of Information Technology 1, Posts and Telecommunication Institute of Technology, Km 10, Nguyen Trai, Ha Dong, Hanoi, Vietnam

    Tien-Dung Do

  3. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Wilayah Persekutuan Kuala Lumpur, 50603, Kuala Lumpur, Malaysia

    Wan Safwani Wan Kamarul Zaman

  4. School of Computer Science, University of Galway, University Road, Galway, H91 TK33, Ireland

    Effirul I. Ramlan

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  1. Mazlee Mazalan
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Mazalan, M., Do, TD., Zaman, W.S.W.K. et al. Machine Learning Approaches for Stem Cells. Curr Stem Cell Rep 9, 43–56 (2023). https://doi.org/10.1007/s40778-023-00228-1

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