Next-generation discovery: empowering organoid research with machine learning, artificial intelligence, and mathematical modeling
Next-generation discovery: empowering organoid research with machine learning, artificial intelligence, and mathematical modeling
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13.03.2026Publisher
ElsevierPublisher Location
AmsterdamMetadata
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Citable Link (Handle URI) 
https://hdl.handle.net/20.500.11811/14143
Abstract
Organoids have rapidly matured into powerful model systems. The field is pushing organoids toward architectural sophistication and functional fidelity, with longitudinal experiments producing ever-larger and more complex datasets. As a result, computational methods have become indispensable for experimental design, data analysis, and predictive modeling, as well as for obtaining mechanistic insights. In this review, we survey recent progress at the interface of organoid research and computational approaches, discuss key challenges on both fronts, and outline future directions to maximize impact in biomedical research through convergent, synergistic efforts.
Subjects
organoids, machine learning, artificial intelligence, mathematical modeling
Classification (DDC)
500 Naturwissenschaften 510 Mathematik 570 Biowissenschaften, Biologie 610 Medizin, GesundheitPublished in
Trends in biotechnology, 2026, pp. 1-17First Publication
https://doi.org/10.1016/j.tibtech.2026.01.009Pushpa Ramesan, Sneha; Boovadira Poonacha, Jasmitha; Pathirana, Dilan; Merkt, Simon; Maass, Christian; Hasenauer, Jan; Reckzeh, Elena S.: Next-generation discovery: empowering organoid research with machine learning, artificial intelligence, and mathematical modeling. In: Trends in biotechnology. 2026, 1-17.
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/14143
Online-Ausgabe in bonndoc: https://hdl.handle.net/20.500.11811/14143
@article{handle:20.500.11811/14143,
author = {{Sneha Pushpa Ramesan} and {Jasmitha Boovadira Poonacha} and {Dilan Pathirana} and {Simon Merkt} and {Christian Maass} and {Jan Hasenauer} and {Elena S. Reckzeh}},
title = {Next-generation discovery: empowering organoid research with machine learning, artificial intelligence, and mathematical modeling},
publisher = {Elsevier},
year = 2026,
month = mar,
journal = {Trends in biotechnology},
volume = 2026,
pages = 1--17,
note = {Organoids have rapidly matured into powerful model systems. The field is pushing organoids toward architectural sophistication and functional fidelity, with longitudinal experiments producing ever-larger and more complex datasets. As a result, computational methods have become indispensable for experimental design, data analysis, and predictive modeling, as well as for obtaining mechanistic insights. In this review, we survey recent progress at the interface of organoid research and computational approaches, discuss key challenges on both fronts, and outline future directions to maximize impact in biomedical research through convergent, synergistic efforts.},
url = {https://hdl.handle.net/20.500.11811/14143}
}
author = {{Sneha Pushpa Ramesan} and {Jasmitha Boovadira Poonacha} and {Dilan Pathirana} and {Simon Merkt} and {Christian Maass} and {Jan Hasenauer} and {Elena S. Reckzeh}},
title = {Next-generation discovery: empowering organoid research with machine learning, artificial intelligence, and mathematical modeling},
publisher = {Elsevier},
year = 2026,
month = mar,
journal = {Trends in biotechnology},
volume = 2026,
pages = 1--17,
note = {Organoids have rapidly matured into powerful model systems. The field is pushing organoids toward architectural sophistication and functional fidelity, with longitudinal experiments producing ever-larger and more complex datasets. As a result, computational methods have become indispensable for experimental design, data analysis, and predictive modeling, as well as for obtaining mechanistic insights. In this review, we survey recent progress at the interface of organoid research and computational approaches, discuss key challenges on both fronts, and outline future directions to maximize impact in biomedical research through convergent, synergistic efforts.},
url = {https://hdl.handle.net/20.500.11811/14143}
}
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