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Stem cell sorting methods based on the Machine Learning algorithms
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  • Marziyeh Mousazadeh,
  • Atieh Jahangiri-Manesh,
  • Hossein Soltaninejad,
  • Karim Rahimian
Marziyeh Mousazadeh
Department of Nanobiotechnology, Faculty of Biological Sciences, Tarbiat Modares University
Atieh Jahangiri-Manesh
Department of Nanobiotechnology, Faculty of Biological Sciences, Tarbiat Modares University
Hossein Soltaninejad
Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University
Karim Rahimian
Department of Biophysics, Faculty of Biological Sciences, Bioinformatics and Computational Omics Lab (BioCOOL), Tarbiat Modares University

Abstract

Artificial intelligence (AI) has been a new, advanced, and fast-growing concept in recent decades. Stem cells are also widely used in regenerative medicine, high-scale cell production, drug screening, stem cell therapies, etc.. Stem cell sorting based on AI methods, machine learning, and deep learning approaches is one of the applications of AI in biology and medicine, which leads to fast, accurate, human-error-free, high-throughput, time-efficient, and cost-efficient approaches. Acquiring big data for the production of reliable, accurate, repeatable, and sensitive sorting algorithms is a bottleneck in AI applications in stem cell sorting, and will become easier with more research in this area. In this review, a new category for AI-based stem cell sorting algorithms is introduced, and the studies are summarized based on different factors such as input data, AI approaches, stem cell types, the goal of the study, and accuracy values.