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ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification.
Kandula, Maciej M; Aldoshin, Alexander D; Singh, Swati; Kolaczyk, Eric D; Kreil, David P.
Affiliation
  • Kandula MM; Institute of Molecular Biotechnology, Boku University Vienna, Austria.
  • Aldoshin AD; Janssen Pharmaceutica NV, Beerse, Belgium.
  • Singh S; Institute of Molecular Biotechnology, Boku University Vienna, Austria.
  • Kolaczyk ED; Institute of Molecular Biotechnology, Boku University Vienna, Austria.
  • Kreil DP; Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, India.
Nucleic Acids Res ; 51(1): e6, 2023 01 11.
Article in En | MEDLINE | ID: mdl-36395816
ABSTRACT
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.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Algorithms / Esophageal Neoplasms Type of study: Prognostic_studies Limits: Humans Language: En Year: 2023 Type: Article

Full text: 1 Database: MEDLINE Main subject: Algorithms / Esophageal Neoplasms Type of study: Prognostic_studies Limits: Humans Language: En Year: 2023 Type: Article