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:Fig1 Xie BMCBioinfo21 22.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Seifert JAMIAOpen20 3.png|240px]]</div>
'''"[[Journal:Popularity and performance of bioinformatics software: The case of gene set analysis|Popularity and performance of bioinformatics software: The case of gene set analysis]]"'''
'''"[[Journal:Informatics-driven quality improvement in the modern histology lab|Informatics-driven quality improvement in the modern histology lab]]"'''


Gene set analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. This work explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive [[information]] from 503 GSA paper references; regarding performance, we introduce a repository of [[Jupyter Notebook]] [[workflow]]s and Shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. ('''[[Journal:Popularity and performance of bioinformatics software: The case of gene set analysis|Full article...]]''')<br />
[[Laboratory information system]]s (LISs) and [[data visualization]] techniques have untapped potential in [[Anatomical pathology|anatomic pathology]] [[Laboratory|laboratories]]. The pre-built functionalities of an LIS do not address all the needs of a modern [[histology]] laboratory. For instance, “go live” is not the end of LIS customization, only the beginning. After closely evaluating various histology lab [[workflows]], we implemented several custom [[Data analysis|data analytics]] dashboards and additional LIS functionalities to monitor and address weaknesses. Herein, we present our experience with LIS and data-tracking solutions that improved trainee education, slide logistics, staffing and instrumentation lobbying, and task tracking. The latter was addressed through the creation of a novel “status board” akin to those seen in inpatient wards. These use-cases can benefit other histology laboratories. ('''[[Journal:Informatics-driven quality improvement in the modern histology lab|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:Popularity and performance of bioinformatics software: The case of gene set analysis|Popularity and performance of bioinformatics software: The case of gene set analysis]]
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Revision as of 17:41, 14 February 2022

Fig2 Seifert JAMIAOpen20 3.png

"Informatics-driven quality improvement in the modern histology lab"

Laboratory information systems (LISs) and data visualization techniques have untapped potential in anatomic pathology laboratories. The pre-built functionalities of an LIS do not address all the needs of a modern histology laboratory. For instance, “go live” is not the end of LIS customization, only the beginning. After closely evaluating various histology lab workflows, we implemented several custom data analytics dashboards and additional LIS functionalities to monitor and address weaknesses. Herein, we present our experience with LIS and data-tracking solutions that improved trainee education, slide logistics, staffing and instrumentation lobbying, and task tracking. The latter was addressed through the creation of a novel “status board” akin to those seen in inpatient wards. These use-cases can benefit other histology laboratories. (Full article...)

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