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<metadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Toward learning the principles of plant gene regulation</dc:title><dc:creator>Zrimec,	Jan	(Avtor)
	</dc:creator><dc:creator>Zelezniak,	Aleksej	(Avtor)
	</dc:creator><dc:creator>Gruden,	Kristina	(Avtor)
	</dc:creator><dc:subject>gene expression prediction</dc:subject><dc:subject>bioinformatics</dc:subject><dc:subject>deep learning</dc:subject><dc:subject>regulatory genomics</dc:subject><dc:description>Advanced machine learning (ML) algorithms produce highly accurate models of gene expression, uncovering novel regulatory features in nucleotide sequences involving multiple cis-regulatory regions across whole genes and structural properties. These broaden our understanding of gene regulation and point to new principles to test and adopt in the field of plant science.

</dc:description><dc:date>2022</dc:date><dc:date>2024-08-06 14:52:48</dc:date><dc:type>Neznano</dc:type><dc:identifier>20157</dc:identifier><dc:identifier>UDK: 60</dc:identifier><dc:identifier>ISSN pri članku: 1360-1385</dc:identifier><dc:identifier>DOI: 10.1016/j.tplants.2022.08.010</dc:identifier><dc:identifier>COBISS_ID: 123482371</dc:identifier><dc:language>sl</dc:language></metadata>
