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Machine Learning-Driven Prediction of Spatiotemporal Dynamics of Active Nuclei During Drosophila Embryogenesis.

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Title: Machine Learning-Driven Prediction of Spatiotemporal Dynamics of Active Nuclei During Drosophila Embryogenesis.
Authors: Malidarreh, Parisa Boodaghi1,2 (AUTHOR), Borad, Priyanshi2,3 (AUTHOR), Rout, Biraaj1,2,3 (AUTHOR), Makridou, Anna4 (AUTHOR), Abbasi, Shiva3,5 (AUTHOR), Nasr, Mohammad Sadegh1,2,6 (AUTHOR), Saurav, Jillur Rahman1,2 (AUTHOR), Fenelon, Kelli D.2,5 (AUTHOR), Veerla, Jai Prakash1,2,3 (AUTHOR), Luber, Jacob M.1,2,4,6 (AUTHOR) jacob.luber@uta.edu, Koromila, Theodora3,4,5 (AUTHOR) theodora.koromila@uta.edu
Source: International Journal of Molecular Sciences. Nov2025, Vol. 26 Issue 21, p10338. 13p.
Database: Academic Search Ultimate
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Molecular+Sciences%22">International Journal of Molecular Sciences</searchLink>. Nov2025, Vol. 26 Issue 21, p10338. 13p.
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        Value: 10.3390/ijms262110338
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        Text: English
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      – TitleFull: Machine Learning-Driven Prediction of Spatiotemporal Dynamics of Active Nuclei During Drosophila Embryogenesis.
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              Text: Nov2025
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