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:Fig2 Heinen BMCBioinfo2020 21.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies|HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Well-characterized biological specimens (biospecimens) of high quality have great potential for the acceleration of and quality improvement in [[Translational research|translational biomedical research]]. To improve accessibility of local [[Sample (material)|specimen]] collections, efforts have been made to create central repositories ([[biobank]]s) and catalogues. Available technical solutions for creating professional local specimen catalogues and connecting them to central systems are cost intensive and/or technically complex to implement. Therefore, the HIV-focused Thematic Translational Unit (TTU) of the German Center for Infection Research (DZIF) developed a [[laboratory information management system]] (LIMS) called HIV Engaged Research Technology (HEnRY) for implementation into the HIV Translational Platform (TP-HIV) at the DZIF and other research networks. ('''[[Journal:HEnRY: A DZIF LIMS tool for the collection and documentation of biospecimens in multicentre studies|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Latest revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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