Implement the Materials Genome Initiative: Machine Learning Assisted Fluorescent Probe Design for Cellular Substructure Staining
Abstract: The Materials Genome Initiative (MGI) is accelerating the pace of advanced materials development by integrating high‐throughput experimentation, database construction, and intelligence computation. Live‐cell imaging agents, such as fluorescent dyes, are exemplary candidates for MGI applications for two reasons: i) they are essential in visualizing cellular structures and functional processes, and ii) the unclear relationship between the chemical structure of fluorescent dyes and their live‐cell imaging properties severely restricts the current trial‐and‐error dye development. Herein, the MGI is followed to present an intelligent combinatorial methodology for predicting the staining cell ability of dyes utilizing machine learning (ML) driven by a structurally diverse combinatorial library. This study demonstrates how to high‐throughput synthesize 1,536 dyes and evaluate their imaging properties to establish a feature dataset for ML. A set of high‐precision ML‐predictors is then successfully modeled for assisting live‐cell staining and endoplasmic reticulum judgment. This approach is believed to bridge the gap between dye structure and corresponding staining behavior, and can accelerate the discovery of novel organelle‐specific stains.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Implement the Materials Genome Initiative: Machine Learning Assisted Fluorescent Probe Design for Cellular Substructure Staining ; day:23 ; month:05 ; year:2023 ; extent:10
Advanced Materials Technologies ; (23.05.2023) (gesamt 10)
- Urheber
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Yang, Yike
Ji, Yumei
Han, Xu
Long, Yunxin
Stewart, Callum
Wen, Yiqiang
Lee, Hok Yeung
Cao, Tian
Han, Jinsong
Chen, Sijie
Li, Linxian
- DOI
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10.1002/admt.202300427
- URN
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urn:nbn:de:101:1-2023052315165392408448
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:59 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Yang, Yike
- Ji, Yumei
- Han, Xu
- Long, Yunxin
- Stewart, Callum
- Wen, Yiqiang
- Lee, Hok Yeung
- Cao, Tian
- Han, Jinsong
- Chen, Sijie
- Li, Linxian
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