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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Swaminathan FrontInGenetics2018 9.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The adoption rate of [[Genomics|genome sequencing]] for clinical diagnostics has been steadily increasing, leading to the possibility of improvement in diagnostic yields. Although [[Laboratory|laboratories]] generate a summary clinical report, sharing raw genomic data with healthcare providers is equally important, both for secondary research studies as well as for a deeper analysis of the data itself, as seen by the efforts from organizations such as American College of Medical Genetics and Genomics, as well as Global Alliance for Genomics and Health. Here, we aim to describe the existing protocol of genomic data sharing between a certified [[clinical laboratory]] and a healthcare provider and highlight some of the lessons learned. This study tracked and subsequently evaluated the data transfer workflow for 19 patients, all of whom consented to be part of this research study and visited the genetics clinic at a tertiary pediatric hospital between April 2016 and December 2016. ('''[[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|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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