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 Reisman EBioinformatics2016 12.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 polyglot approach to bioinformatics data integration: A phylogenetic analysis of HIV-1|A polyglot approach to bioinformatics data integration: A phylogenetic analysis of HIV-1]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


As [[sequencing]] technologies continue to drop in price and increase in throughput, new challenges emerge for the management and accessibility of genomic sequence data. We have developed a pipeline for facilitating the storage, retrieval, and subsequent analysis of molecular data, integrating both sequence and metadata. Taking a polyglot approach involving multiple languages, libraries, and persistence mechanisms, sequence data can be aggregated from publicly available and local repositories. Data are exposed in the form of a RESTful web service, formatted for easy querying, and retrieved for downstream analyses. As a proof of concept, we have developed a resource for annotated HIV-1 sequences. Phylogenetic analyses were conducted for >6,000 HIV-1 sequences revealing spatial and temporal factors influence the evolution of the individual genes uniquely. Nevertheless, signatures of origin can be extrapolated even despite increased globalization. The approach developed here can easily be customized for any species of interest. ('''[[Journal:A polyglot approach to bioinformatics data integration: A phylogenetic analysis of HIV-1|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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