Binary Simplification as an Effective Tool in Metabolomics Data Analysis Article Swipe
Francisco Traquete
,
João Luz
,
Carlos Cordeiro
,
Marta Sousa Silva
,
António E. N. Ferreira
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/metabo11110788
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/metabo11110788
Metabolomics aims to perform a comprehensive identification and quantification of the small molecules present in a biological system. Due to metabolite diversity in concentration, structure, and chemical characteristics, the use of high-resolution methodologies, such as mass spectrometry (MS) or nuclear magnetic resonance (NMR), is required. In metabolomics data analysis, suitable data pre-processing, and pre-treatment procedures are fundamental, with subsequent steps aiming at highlighting the significant biological variation between samples over background noise. Traditional data analysis focuses primarily on the comparison of the features’ intensity values. However, intensity data are highly variable between experimental batches, instruments, and pre-processing methods or parameters. The aim of this work was to develop a new pre-treatment method for MS-based metabolomics data, in the context of sample profiling and discrimination, considering only the occurrence of spectral features, encoding feature presence as 1 and absence as 0. This “Binary Simplification” encoding (BinSim) was used to transform several benchmark datasets before the application of clustering and classification methods. The performance of these methods after the BinSim pre-treatment was consistently as good as and often better than after different combinations of traditional, intensity-based, pre-treatments. Binary Simplification is, therefore, a viable pre-treatment procedure that effectively simplifies metabolomics data-analysis pipelines.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/metabo11110788
- https://www.mdpi.com/2218-1989/11/11/788/pdf?version=1637545205
- OA Status
- gold
- Cited By
- 10
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3211621168
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Raw OpenAlex JSON
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https://openalex.org/W3211621168Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/metabo11110788Digital Object Identifier
- Title
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Binary Simplification as an Effective Tool in Metabolomics Data AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-18Full publication date if available
- Authors
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Francisco Traquete, João Luz, Carlos Cordeiro, Marta Sousa Silva, António E. N. FerreiraList of authors in order
- Landing page
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https://doi.org/10.3390/metabo11110788Publisher landing page
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https://www.mdpi.com/2218-1989/11/11/788/pdf?version=1637545205Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2218-1989/11/11/788/pdf?version=1637545205Direct OA link when available
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Metabolomics, Computer science, Data mining, Binary number, Context (archaeology), Cluster analysis, Binary data, Pattern recognition (psychology), Artificial intelligence, Bioinformatics, Mathematics, Biology, Arithmetic, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 4, 2022: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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