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ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification
With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841426/ https://www.ncbi.nlm.nih.gov/pubmed/36395816 http://dx.doi.org/10.1093/nar/gkac988 |
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author | Kańduła, Maciej M Aldoshin, Alexander D Singh, Swati Kolaczyk, Eric D Kreil, David P |
author_facet | Kańduła, Maciej M Aldoshin, Alexander D Singh, Swati Kolaczyk, Eric D Kreil, David P |
author_sort | Kańduła, Maciej M |
collection | PubMed |
description | With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed. |
format | Online Article Text |
id | pubmed-9841426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98414262023-01-18 ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification Kańduła, Maciej M Aldoshin, Alexander D Singh, Swati Kolaczyk, Eric D Kreil, David P Nucleic Acids Res Methods Online With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed. Oxford University Press 2022-11-18 /pmc/articles/PMC9841426/ /pubmed/36395816 http://dx.doi.org/10.1093/nar/gkac988 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Online Kańduła, Maciej M Aldoshin, Alexander D Singh, Swati Kolaczyk, Eric D Kreil, David P ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification |
title | ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification |
title_full | ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification |
title_fullStr | ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification |
title_full_unstemmed | ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification |
title_short | ViLoN—a multi-layer network approach to data integration demonstrated for patient stratification |
title_sort | vilon—a multi-layer network approach to data integration demonstrated for patient stratification |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841426/ https://www.ncbi.nlm.nih.gov/pubmed/36395816 http://dx.doi.org/10.1093/nar/gkac988 |
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