Graph embedding on mass spectrometry- and sequencing-based biomedical data
Abstract Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05612-6 |
| _version_ | 1797362825434234880 |
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| author | Edwin Alvarez-Mamani Reinhard Dechant César A. Beltran-Castañón Alfredo J. Ibáñez |
| author_facet | Edwin Alvarez-Mamani Reinhard Dechant César A. Beltran-Castañón Alfredo J. Ibáñez |
| author_sort | Edwin Alvarez-Mamani |
| collection | DOAJ |
| description | Abstract Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein–protein interaction networks and predicting novel drug functions. |
| first_indexed | 2024-03-08T16:12:00Z |
| format | Article |
| id | doaj.art-0ac19e335e13460d9155682727e86283 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-08T16:12:00Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-0ac19e335e13460d9155682727e862832024-01-07T12:49:34ZengBMCBMC Bioinformatics1471-21052024-01-0125111910.1186/s12859-023-05612-6Graph embedding on mass spectrometry- and sequencing-based biomedical dataEdwin Alvarez-Mamani0Reinhard Dechant1César A. Beltran-Castañón2Alfredo J. Ibáñez3Engineering Department, Pontificia Universidad Católica del PerúInstitute for Omics Sciences and Applied Biotechnology (ICOBA PUCP), Pontificia Universidad Católica del PerúEngineering Department, Pontificia Universidad Católica del PerúInstitute for Omics Sciences and Applied Biotechnology (ICOBA PUCP), Pontificia Universidad Católica del PerúAbstract Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein–protein interaction networks and predicting novel drug functions.https://doi.org/10.1186/s12859-023-05612-6Graph embeddingBiomedical dataBiological network |
| spellingShingle | Edwin Alvarez-Mamani Reinhard Dechant César A. Beltran-Castañón Alfredo J. Ibáñez Graph embedding on mass spectrometry- and sequencing-based biomedical data BMC Bioinformatics Graph embedding Biomedical data Biological network |
| title | Graph embedding on mass spectrometry- and sequencing-based biomedical data |
| title_full | Graph embedding on mass spectrometry- and sequencing-based biomedical data |
| title_fullStr | Graph embedding on mass spectrometry- and sequencing-based biomedical data |
| title_full_unstemmed | Graph embedding on mass spectrometry- and sequencing-based biomedical data |
| title_short | Graph embedding on mass spectrometry- and sequencing-based biomedical data |
| title_sort | graph embedding on mass spectrometry and sequencing based biomedical data |
| topic | Graph embedding Biomedical data Biological network |
| url | https://doi.org/10.1186/s12859-023-05612-6 |
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