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:FigA4 Joppich PeerJ2019 7.jpg|240px]]</div>
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'''"[[Journal:From command-line bioinformatics to bioGUI|From command-line bioinformatics to bioGUI]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


[[Bioinformatics]] is a highly interdisciplinary field providing informatics applications for scientists from many disciplines. Installing and starting applications on the command line (CL) is inconvenient and inefficient for many scientists. Nonetheless, most methods are implemented with a command-line interface only. Providing a graphical user interface (GUI) for bioinformatics applications is one step toward routinely making CL-only applications more readily available to scientists, yielding a positive step toward more effective interdisciplinary work. With our bioGUI framework, we address two main problems of using CL bioinformatics applications. First, many tools work on UNIX-based systems only, while many scientists use Microsoft Windows. Second, scientists refrain from using CL tools, which, despite their reservations, could well support them in their research. With bioGUI install modules and templates, installing and using CL tools is made possible for most scientists, even on Windows, due to bioGUI’s support for Windows Subsystem for Linux. In addition, bioGUI templates can easily be created, making the bioGUI framework highly rewarding for developers. From the bioGUI repository it is possible to download, install, and use bioinformatics tools with just a few clicks. ('''[[Journal:From command-line bioinformatics to bioGUI|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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