In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

Pathway analysis aids interpretation of large-scale gene expression data, but existing algorithms fall short of providing robust pathway identification. The method introduced here includes coexpression analysis and gene importance estimation to robustly identify relevant pathways and biomarkers for...

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Main Authors: Ivan V. Ozerov, Ksenia V. Lezhnina, Evgeny Izumchenko, Artem V. Artemov, Sergey Medintsev, Quentin Vanhaelen, Alexander Aliper, Jan Vijg, Andreyan N. Osipov, Ivan Labat, Michael D. West, Anton Buzdin, Charles R. Cantor, Yuri Nikolsky, Nikolay Borisov, Irina Irincheeva, Edward Khokhlovich, David Sidransky, Miguel Luiz Camargo, Alex Zhavoronkov
Format: Article
Language:English
Published: Nature Publishing Group 2016-11-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/ncomms13427
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spelling doaj-2c57748e19064775b999e9d5f44aa2b92021-05-11T11:03:12ZengNature Publishing GroupNature Communications2041-17232016-11-017111110.1038/ncomms13427In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker developmentIvan V. Ozerov0Ksenia V. Lezhnina1Evgeny Izumchenko2Artem V. Artemov3Sergey Medintsev4Quentin Vanhaelen5Alexander Aliper6Jan Vijg7Andreyan N. Osipov8Ivan Labat9Michael D. West10Anton Buzdin11Charles R. Cantor12Yuri Nikolsky13Nikolay Borisov14Irina Irincheeva15Edward Khokhlovich16David Sidransky17Miguel Luiz Camargo18Alex Zhavoronkov19Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternPharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternDepartment of Otolaryngology, The Johns Hopkins University, School of Medicine, Head and Neck Cancer ResearchPharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternPharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternPharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternPharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternDepartment of Genetics, Albert Einstein College of MedicinePharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternBioTime, Inc.BioTime, Inc.Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternDepartment of Biomedical Engineering, Boston UniversityPharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternPharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternNutrition and Metabolic Health group, Nestlé Institute of Health SciencesNovartis Institutes for BioMedical ResearchDepartment of Otolaryngology, The Johns Hopkins University, School of Medicine, Head and Neck Cancer ResearchNovartis Institutes for BioMedical ResearchPharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at EasternPathway analysis aids interpretation of large-scale gene expression data, but existing algorithms fall short of providing robust pathway identification. The method introduced here includes coexpression analysis and gene importance estimation to robustly identify relevant pathways and biomarkers for patient stratification.https://doi.org/10.1038/ncomms13427
collection DOAJ
language English
format Article
sources DOAJ
author Ivan V. Ozerov
Ksenia V. Lezhnina
Evgeny Izumchenko
Artem V. Artemov
Sergey Medintsev
Quentin Vanhaelen
Alexander Aliper
Jan Vijg
Andreyan N. Osipov
Ivan Labat
Michael D. West
Anton Buzdin
Charles R. Cantor
Yuri Nikolsky
Nikolay Borisov
Irina Irincheeva
Edward Khokhlovich
David Sidransky
Miguel Luiz Camargo
Alex Zhavoronkov
spellingShingle Ivan V. Ozerov
Ksenia V. Lezhnina
Evgeny Izumchenko
Artem V. Artemov
Sergey Medintsev
Quentin Vanhaelen
Alexander Aliper
Jan Vijg
Andreyan N. Osipov
Ivan Labat
Michael D. West
Anton Buzdin
Charles R. Cantor
Yuri Nikolsky
Nikolay Borisov
Irina Irincheeva
Edward Khokhlovich
David Sidransky
Miguel Luiz Camargo
Alex Zhavoronkov
In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
Nature Communications
author_facet Ivan V. Ozerov
Ksenia V. Lezhnina
Evgeny Izumchenko
Artem V. Artemov
Sergey Medintsev
Quentin Vanhaelen
Alexander Aliper
Jan Vijg
Andreyan N. Osipov
Ivan Labat
Michael D. West
Anton Buzdin
Charles R. Cantor
Yuri Nikolsky
Nikolay Borisov
Irina Irincheeva
Edward Khokhlovich
David Sidransky
Miguel Luiz Camargo
Alex Zhavoronkov
author_sort Ivan V. Ozerov
title In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
title_short In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
title_full In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
title_fullStr In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
title_full_unstemmed In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development
title_sort in silico pathway activation network decomposition analysis (ipanda) as a method for biomarker development
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2016-11-01
description Pathway analysis aids interpretation of large-scale gene expression data, but existing algorithms fall short of providing robust pathway identification. The method introduced here includes coexpression analysis and gene importance estimation to robustly identify relevant pathways and biomarkers for patient stratification.
url https://doi.org/10.1038/ncomms13427
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