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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig6 Ogle FrontBigData2021 4.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Named data networking for genomics data management and integrated workflows|Named data networking for genomics data management and integrated workflows]]"'''
'''"[[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]]"'''


Advanced [[Imaging informatics|imaging]] and [[DNA sequencing]] technologies now enable the diverse biology community to routinely generate and analyze terabytes of high-resolution biological data. The community is rapidly heading toward the petascale in single-investigator [[laboratory]] settings. As evidence, the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) central DNA sequence repository alone contains over 45 petabytes of biological data. Given the geometric growth of this and other [[genomics]] repositories, an exabyte of mineable biological data is imminent. The challenges of effectively utilizing these datasets are enormous, as they are not only large in size but also stored in various geographically distributed repositories such as those hosted by the NCBI, as well as in the DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and NASA’s GeneLab. ('''[[Journal:Named data networking for genomics data management and integrated workflows|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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