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:Fig2 ReillyInformatics2015 2-3.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:MaPSeq, a service-oriented architecture for genomics research within an academic biomedical research institution|MaPSeq, a service-oriented architecture for genomics research within an academic biomedical research institution]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


[[Genomics]] research presents technical, computational, and analytical challenges that are well recognized. Less recognized are the complex sociological, psychological, cultural, and political challenges that arise when genomics research takes place within a large, decentralized academic institution. In this paper, we describe a Service-Oriented Architecture (SOA) — MaPSeq — that was conceptualized and designed to meet the diverse and evolving computational workflow needs of genomics researchers at our large, [[hospital]]-affiliated, academic research institution. We present the institutional challenges that motivated the design of MaPSeq before describing the architecture and functionality of MaPSeq. We then discuss SOA solutions and conclude that approaches such as MaPSeq enable efficient and effective computational workflow execution for genomics research and for any type of academic biomedical research that requires complex, computationally-intense workflows. ('''[[Journal:MaPSeq, a service-oriented architecture for genomics research within an academic biomedical research institution|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...)
Recently featured: