Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Konnick PractLabMed2020 21.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:The regulatory landscape of precision oncology laboratory medicine in the United States: Perspective on the past five years and considerations for future regulation|The regulatory landscape of precision oncology laboratory medicine in the United States: Perspective on the past five years and considerations for future regulation]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The [[Regulatory compliance|regulatory]] landscape for precision [[oncology]] in the United States is complicated, with multiple governmental regulatory agencies with different scopes of jurisdiction. Several regulatory proposals have been introduced since the [[Food and Drug Administration]] released draft guidance to regulate [[laboratory developed test]]s in 2014. Key aspects of the most recent proposals and discussion of central arguments related to the regulation of precision oncology laboratory tests provides insight to stakeholders for future discussions related to regulation of [[laboratory]] [[Medical test|tests]]. ('''[[Journal:The regulatory landscape of precision oncology laboratory medicine in the United States: Perspective on the past five years and considerations for future regulation|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
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Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
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