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 Cicek FrontPubHealth2020 8.jpg|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:Mini-review of laboratory operations in biobanking: Building biobanking resources for translational research|Mini-review of laboratory operations in biobanking: Building biobanking resources for translational research]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


[[Biobank]]s have become integral to improving [[Public health|population health]]. We are in a new era in medicine as patients, health professionals, and researchers increasingly collaborate to gain new knowledge and explore new paradigms for diagnosing and treating disease. Many large-scale biobanking efforts are underway worldwide at the institutional, national, and even international level. When linked with subject data from questionnaires and medical records, biobanks serve as valuable resources in [[translational research]]. A biobank must have high-quality biospecimens that meet researcher's needs. Biobank [[laboratory]] operations require an enormous amount of support, from lab and storage space, information technology expertise, and a [[laboratory information management system]] to logistics for biospecimen tracking, [[quality management system]]s, and appropriate facilities. ('''[[Journal:Mini-review of laboratory operations in biobanking: Building biobanking resources for translational research|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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