gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes
Count data, such as gene expression and microbiome composition, play a significant role in various diseases, including cancer, obesity, inflammatory bowel disease, and mental health disorders. For instance, understanding the differences in microbial abundance between patients is essential for uncove...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S200103702500296X |
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| author | Leon Fehse Mohammad Tajabadi Roman Martin Hajo Holzmann Dominik Heider |
| author_facet | Leon Fehse Mohammad Tajabadi Roman Martin Hajo Holzmann Dominik Heider |
| author_sort | Leon Fehse |
| collection | DOAJ |
| description | Count data, such as gene expression and microbiome composition, play a significant role in various diseases, including cancer, obesity, inflammatory bowel disease, and mental health disorders. For instance, understanding the differences in microbial abundance between patients is essential for uncovering the microbiome's impact on these conditions. Differential abundance analysis (DAA) can detect significant changes between groups of patients. However, since individuals have unique microbial fingerprints that could potentially be identifiable, microbiome data must be treated as sensitive patient data, which poses problems for collaborative studies in the medical field. In this work, we introduce gLinDA, a global differential abundance analysis tool that employs a privacy-preserving swarm learning approach for the analysis of distributed datasets. gLinDA maintains predictive performance while safeguarding patient sensitive data. |
| format | Article |
| id | doaj-art-30ea5dcda0d94055ac57b7b7efd44ade |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-30ea5dcda0d94055ac57b7b7efd44ade2025-08-20T02:56:20ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01273456346310.1016/j.csbj.2025.07.031gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomesLeon Fehse0Mohammad Tajabadi1Roman Martin2Hajo Holzmann3Dominik Heider4University of Münster, Institute of Medical Informatics, Albert-Schweitzer-Campus 1, Münster, 48149, North Rhine-Westphalia, Germany; Institute for Computer Science, Heinrich-Heine-University Düsseldorf, Graf-Adolf-Str. 63, Düsseldorf, 40215, North Rhine-Westphalia, GermanyUniversity of Münster, Institute of Medical Informatics, Albert-Schweitzer-Campus 1, Münster, 48149, North Rhine-Westphalia, Germany; Institute for Computer Science, Heinrich-Heine-University Düsseldorf, Graf-Adolf-Str. 63, Düsseldorf, 40215, North Rhine-Westphalia, GermanyUniversity of Münster, Institute of Medical Informatics, Albert-Schweitzer-Campus 1, Münster, 48149, North Rhine-Westphalia, Germany; Institute for Computer Science, Heinrich-Heine-University Düsseldorf, Graf-Adolf-Str. 63, Düsseldorf, 40215, North Rhine-Westphalia, GermanyDepartment of Mathematics and Computer Science, Philipps University of Marburg, Hans-Meerwein-Str. 6, Marburg, 35032, Hesse, GermanyUniversity of Münster, Institute of Medical Informatics, Albert-Schweitzer-Campus 1, Münster, 48149, North Rhine-Westphalia, Germany; Institute for Computer Science, Heinrich-Heine-University Düsseldorf, Graf-Adolf-Str. 63, Düsseldorf, 40215, North Rhine-Westphalia, Germany; Department of Mathematics and Computer Science, Philipps University of Marburg, Hans-Meerwein-Str. 6, Marburg, 35032, Hesse, Germany; Corresponding author at: University of Münster, Institute of Medical Informatics, Albert-Schweitzer-Campus 1, Münster, 48149, North Rhine-Westphalia, Germany.Count data, such as gene expression and microbiome composition, play a significant role in various diseases, including cancer, obesity, inflammatory bowel disease, and mental health disorders. For instance, understanding the differences in microbial abundance between patients is essential for uncovering the microbiome's impact on these conditions. Differential abundance analysis (DAA) can detect significant changes between groups of patients. However, since individuals have unique microbial fingerprints that could potentially be identifiable, microbiome data must be treated as sensitive patient data, which poses problems for collaborative studies in the medical field. In this work, we introduce gLinDA, a global differential abundance analysis tool that employs a privacy-preserving swarm learning approach for the analysis of distributed datasets. gLinDA maintains predictive performance while safeguarding patient sensitive data.http://www.sciencedirect.com/science/article/pii/S200103702500296XMicrobiomeDifferential abundance analysisSwarm learning |
| spellingShingle | Leon Fehse Mohammad Tajabadi Roman Martin Hajo Holzmann Dominik Heider gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes Computational and Structural Biotechnology Journal Microbiome Differential abundance analysis Swarm learning |
| title | gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes |
| title_full | gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes |
| title_fullStr | gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes |
| title_full_unstemmed | gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes |
| title_short | gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes |
| title_sort | glinda a privacy preserving swarm learning toolbox for differential abundance analysis of microbiomes |
| topic | Microbiome Differential abundance analysis Swarm learning |
| url | http://www.sciencedirect.com/science/article/pii/S200103702500296X |
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