Difference between revisions of "Template:Article of the week"

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A '''[[biobank]]''' is a collection of biological samples (usually human) for use in research. The samples may also include personal medical and genealogical data. Sites for these collections come in different forms, typically based on the types of samples being stored and the scientific domain associated with them. These sites can be loosely categorized into two types: those based on biological specimens from patients and donors, and those specifically designed to aid in population-based research.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Biobanks give researchers access to data representing larger numbers of people than could be analyzed in previously used systems. Furthermore, samples in biobanks and the data derived from those samples can often be used by multiple researchers for multiple purposes. Large collections of samples representing tens or hundreds of thousands of individuals are necessary to conduct certain studies. However, these activities come with their share of questions regarding research and medical ethics, and they have provoked discussions in some community circles. While viewpoints on what constitutes appropriate biobank ethics diverge, consensus has been reached that relying on biobanks without carefully considered governing principles and policies could negatively impact communities participating in biobank programs. ('''[[Biobank|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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''Recently featured'':
''Recently featured'': [[Translational research]], [[Rural health clinic]], [[Picture archiving and communication system]]
{{flowlist |
* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
}}

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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