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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:FigA4 Joppich PeerJ2019 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:From command-line bioinformatics to bioGUI|From command-line bioinformatics to bioGUI]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


[[Bioinformatics]] is a highly interdisciplinary field providing informatics applications for scientists from many disciplines. Installing and starting applications on the command line (CL) is inconvenient and inefficient for many scientists. Nonetheless, most methods are implemented with a command-line interface only. Providing a graphical user interface (GUI) for bioinformatics applications is one step toward routinely making CL-only applications more readily available to scientists, yielding a positive step toward more effective interdisciplinary work. With our bioGUI framework, we address two main problems of using CL bioinformatics applications. First, many tools work on UNIX-based systems only, while many scientists use Microsoft Windows. Second, scientists refrain from using CL tools, which, despite their reservations, could well support them in their research. With bioGUI install modules and templates, installing and using CL tools is made possible for most scientists, even on Windows, due to bioGUI’s support for Windows Subsystem for Linux. In addition, bioGUI templates can easily be created, making the bioGUI framework highly rewarding for developers. From the bioGUI repository it is possible to download, install, and use bioinformatics tools with just a few clicks. ('''[[Journal:From command-line bioinformatics to bioGUI|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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