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:Fig3 Panahiazar JofBiomedInformatics2017 72-8.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Huang iScience2022 25-8.jpg|240px]]</div>
'''"[[Journal:Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO)|Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO)]]"'''
'''"[[Journal:Elegancy: Digitizing the wisdom from laboratories to the cloud with free no-code platform|Elegancy: Digitizing the wisdom from laboratories to the cloud with free no-code platform]]"'''


A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1.3 million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table.
One of the top priorities in any [[laboratory]] is [[Archival informatics|archiving]] experimental data in the most secure, efficient, and errorless way. It is especially important to those in chemical and biological research, for it is more likely to damage experiment records. In addition, the transmission of experiment results from paper to electronic devices is time-consuming and redundant. Therefore, we introduce an [[Open-source software|open-source]] no-code [[electronic laboratory notebook]] (ELN), Elegancy, a [[Cloud computing|cloud-based]]/standalone web service distributed as a Docker image. Elegancy fits all laboratories but is specially equipped with several features benefitting biochemical laboratories. It can be accessed via various web browsers, allowing researchers to upload photos or audio recordings directly from their mobile devices. Elegancy also contains a meeting arrangement module, audit/revision control, and laboratory supply management system. We believe Elegancy could help the scientific research community gather evidence, share information, reorganize knowledge, and digitize laboratory works with greater ease and security ... ('''[[Journal:Elegancy: Digitizing the wisdom from laboratories to the cloud with free no-code platform|Full article...]]''')<br />
 
All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. ('''[[Journal:Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO)|Full article...]]''')<br />
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Latest revision as of 17:08, 10 April 2023

Fig1 Huang iScience2022 25-8.jpg

"Elegancy: Digitizing the wisdom from laboratories to the cloud with free no-code platform"

One of the top priorities in any laboratory is archiving experimental data in the most secure, efficient, and errorless way. It is especially important to those in chemical and biological research, for it is more likely to damage experiment records. In addition, the transmission of experiment results from paper to electronic devices is time-consuming and redundant. Therefore, we introduce an open-source no-code electronic laboratory notebook (ELN), Elegancy, a cloud-based/standalone web service distributed as a Docker image. Elegancy fits all laboratories but is specially equipped with several features benefitting biochemical laboratories. It can be accessed via various web browsers, allowing researchers to upload photos or audio recordings directly from their mobile devices. Elegancy also contains a meeting arrangement module, audit/revision control, and laboratory supply management system. We believe Elegancy could help the scientific research community gather evidence, share information, reorganize knowledge, and digitize laboratory works with greater ease and security ... (Full article...)

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