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

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text)
(Updated article of the week text)
(80 intermediate revisions by the same user not shown)
Line 1: Line 1:
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Tomlinson JofMolDiag2022 S1525-1578-22.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:A model for design and implementation of a laboratory information management system specific to molecular pathology laboratory operations|A model for design and implementation of a laboratory information management system specific to molecular pathology laboratory operations]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The Molecular Pathology Section of Cleveland Clinic (Cleveland, OH) has undergone enhancement of its testing portfolio and processes. An electronic- and paper-based [[Information management|data management]] system was replaced with a commercially available [[laboratory information management system]] (LIMS) solution, a separate [[bioinformatics]] platform, customized test-interpretation applications, a dedicated [[Accessioning (medical)|accessioning]] service, and a results-releasing solution. The LIMS solution manages complex [[workflow]]s, large-scale data packets, and process automation. However, a customized approach was required for the LIMS since a survey of commercially available off-the-shelf (COTS) software solutions revealed none met the diverse and complex needs of Cleveland Clinic's [[molecular pathology]] service ... ('''[[Journal:A model for design and implementation of a laboratory information management system specific to molecular pathology laboratory operations|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 />
<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:DigiPatICS: Digital pathology transformation of the Catalan Health Institute network of eight hospitals - Planning, implementation, and preliminary results|DigiPatICS: Digital pathology transformation of the Catalan Health Institute network of eight hospitals - Planning, implementation, and preliminary results]]
* [[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:Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation|Structure-based knowledge acquisition from electronic lab notebooks for research data provenance documentation]]
* [[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:Food informatics: Review of the current state-of-the-art, revised definition, and classification into the research landscape|Food informatics: Review of the current state-of-the-art, revised definition, and classification into the research landscape]]
* [[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]]
}}
}}

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...)
Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=63551"