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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:Fig1 Signoroni NatComm23 14.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:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]"'''


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 />
Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria|bacterial]] [[Cell culture|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Full article...]]''')<br />
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Latest revision as of 15:02, 3 June 2024

Fig1 Signoroni NatComm23 14.png

"Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology"

Full laboratory automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... (Full article...)
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