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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig4 Auer CytometryPartA2018 93-7.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:ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis|ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Multicolor image analysis finds its applications in a broad range of biological studies. Specifically, multiplex [[wikipedia:Fluorescence in situ hybridization|fluorescence ''in situ'' hybridization]] (M‐FISH) for chromosome painting facilitates the analysis of individual chromosomes in complex metaphase spreads and is widely used to detect both numerical and structural aberrations. While this is well established for human and mouse [[wikipedia:Karyotype|karyotypes]], for which species sophisticated software and analysis tools are available, other organisms and species are less well served. Commercially available software is proprietary and not easily adaptable to other karyotypes. Therefore, a publicly available open-source software that combines flexibility and customizable functionalities is needed. Here we present such a tool, called “ChromaWizard,” which is based on popular scientific image analysis libraries (OpenCV, scikit‐image, and NumPy). We demonstrate its functionality on the example of primary Chinese hamster (''Cricetulus griseus'') fibroblasts metaphase spreads and on Chinese hamster ovary cell lines, known for their large number of chromosomal rearrangements. ('''[[Journal:ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis|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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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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