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Applying Machine Learning to Stem Cell Culture and Differentiation
Author(s) -
Ashraf Mishal,
Khalilitousi Mohammadali,
Laksman Zachary
Publication year - 2021
Publication title -
current protocols
Language(s) - English
Resource type - Journals
ISSN - 2691-1299
DOI - 10.1002/cpz1.261
Subject(s) - workflow , artificial intelligence , computer science , stem cell , stem cell biology , machine learning , computational biology , biology , embryo , reproductive technology , database , embryogenesis , genetics , microbiology and biotechnology
Abstract Machine learning techniques are increasingly becoming incorporated into biological research workflows in a variety of disciplines, most notably cancer research and drug discovery. Efforts in stem cell research comparatively lag behind. We detail key paradigms in machine learning, with a focus on equipping stem cell biologists with the understanding necessary to begin conceptualizing and designing machine learning workflows within their own domain of expertise. Supervised approaches in both regression and classification as well as unsupervised clustering techniques are all covered, with examples from across the biological sciences. High‐throughput, high‐content, multiplex assays for data acquisition are also discussed in the form of single‐cell RNA sequencing and image‐based approaches. Lastly, potential applications in stem cell biology, including the development of novel cell types, and improving model maturation are also discussed. Machine learning approaches applied in stem cell biology show promise in accelerating progress in developmental biology, drug screening, disease modeling, and personalized medicine. © 2021 Wiley Periodicals LLC.