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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 where 1266=1266 or not 5936=5936-- qfcb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 where 9554=9554 and 1819=(select (case when (1819=1819) then 1819 else (select 4671 union select 3682) end))-- baqs] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 and 8623=8623# mtsu] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 and 8623=8623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 and 6143=8332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 where 7735=7735 or not 4799=3541-- rhxt] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 and 4781=(select (case when (4781=6244) then 4781 else (select 6244 union select 9918) end))-- lcaz] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 or not 5936=5936-- miel] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 and 1819=(select (case when (1819=1819) then 1819 else (select 4671 union select 3682) end))-- qpln] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 or not 5936=5936# cpou] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 and 7833=4416-- xlsn] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 where 9649=9649 and 2714=(select (case when (2714=6666) then 2714 else (select 6666 union select 4895) end))-- ejaf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 or not 5670=8100# fxoq] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 or not 6347=1706-- gnjn] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 and 8623=8623-- yesg] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 and 2138=3169# zebx] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 where 2289=2289 and 9793=8976-- mdxf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 where 1521=1521 and 8623=8623-- wmsp] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 or not 4684=8950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2018. [DOI: 10.1093/nar/gky1131 or not 5936=5936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Damian Szklarczyk
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Annika L Gable
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - David Lyon
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Alexander Junge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Stefan Wyder
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Jaime Huerta-Cepas
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM)—Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), 28223 Madrid, Spain
| | - Milan Simonovic
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
| | - Nadezhda T Doncheva
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
- Center for non-coding RNA in Technology and Health, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - John H Morris
- Resource on Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA 94158-2517, USA
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69117 Heidelberg, Germany
- Max Delbrück Centre for Molecular Medicine, 13125 Berlin, Germany
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Lars J Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland
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Hu WP, Zeng YY, Zuo YH, Zhang J. Identification of novel candidate genes involved in the progression of emphysema by bioinformatic methods. Int J Chron Obstruct Pulmon Dis 2018; 13:3733-3747. [PMID: 30532529 PMCID: PMC6241693 DOI: 10.2147/copd.s183100] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Purpose By reanalyzing the gene expression profile GSE76925 in the Gene Expression Omnibus database using bioinformatic methods, we attempted to identify novel candidate genes promoting the development of emphysema in patients with COPD. Patients and methods According to the Quantitative CT data in GSE76925, patients were divided into mild emphysema group (%LAA-950<20%, n=12) and severe emphysema group (%LAA-950>50%, n=11). Differentially expressed genes (DEGs) were identified using Agilent GeneSpring GX v11.5 (corrected P-value <0.05 and |Fold Change|>1.3). Known driver genes of COPD were acquired by mining literatures and retrieving databases. Direct protein–protein interaction network (PPi) of DEGs and known driver genes was constructed by STRING.org to screen the DEGs directly interacting with driver genes. In addition, we used STRING.org to obtain the first-layer proteins interacting with DEGs’ products and constructed the indirect PPi of these interaction proteins. By merging the indirect PPi with driver genes’ PPi using Cytoscape v3.6.1, we attempted to discover potential pathways promoting emphysema’s development. Results All the patients had COPD with severe airflow limitation (age=62±8, FEV1%=28±12). A total of 57 DEGs (including 12 pseudogenes) and 135 known driving genes were identified. Direct PPi suggested that GPR65, GNB4, P2RY13, NPSR1, BCR, BAG4, and IMPDH2 were potential pathogenic genes. GPR65 could regulate the response of immune cells to the acidic microenvironment, and NPSR1’s expression on eosinophils was associated with asthma’s severity and IgE level. Indirect merging PPi demonstrated that the interacting network of TP53, IL8, CCR2, HSPA1A, ELANE, PIK3CA was associated with the development of emphysema. IL8, ELANE, and PIK3CA were molecules involved in the pathological mechanisms of emphysema, which also in return proved the role of TP53 in emphysema. Conclusion Candidate genes such as GPR65, NPSR1, and TP53 may be involved in the progression of emphysema.
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Affiliation(s)
- Wei-Ping Hu
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,
| | - Ying-Ying Zeng
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,
| | - Yi-Hui Zuo
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,
| | - Jing Zhang
- Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China,
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Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods. World J Surg Oncol 2018; 16:223. [PMID: 30428899 PMCID: PMC6237021 DOI: 10.1186/s12957-018-1519-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 10/24/2018] [Indexed: 02/05/2023] Open
Abstract
Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. Electronic supplementary material The online version of this article (10.1186/s12957-018-1519-y) contains supplementary material, which is available to authorized users.
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Abstract
Viruses utilize a number of host factors in order to carry out their replication cycles. Influenza A virus (IAV) and human respiratory syncytial virus (RSV) both infect the tissues of the respiratory tract, and as such we hypothesize that they might require similar host factors. Several published genome-wide screens have identified putative IAV host factors; however, there is significant discordance between their hits. In order to build on this work, we integrated a variety of "OMICS" data sources using two complementary network analyses, yielding 51 genes enriched for both IAV and RSV replication. We designed a targeted small interfering RNA (siRNA)-based assay to screen these genes against IAV under robust conditions and identified 13 genes supported by two IAV subtypes in both primary and transformed human lung cells. One of these hits, RNA binding motif 14 (RBM14), was validated as a required host factor and furthermore was shown to relocalize to the nucleolus upon IAV infection but not with other viruses. Additionally, the IAV NS1 protein is both necessary and sufficient for RBM14 relocalization, and relocalization also requires the double-stranded RNA (dsRNA) binding capacity of NS1. This work reports the discovery of a new host requirement for IAV replication and exposes a novel example of interplay between IAV NS1 and the host protein, RBM14.IMPORTANCE Influenza A virus (IAV) and respiratory syncytial virus (RSV) present major global disease burdens. There are high economic costs associated with morbidity as well as significant mortality rates, especially in developing countries, in children, and in the elderly. There are currently limited therapeutic options for these viruses, which underscores the need for novel research into virus biology that may lead to the discovery of new therapeutic approaches. This work extends existing research into host factors involved in virus replication and explores the interaction between IAV and one such host factor, RBM14. Further study to fully characterize this interaction may elucidate novel mechanisms used by the virus during its replication cycle and open new avenues for understanding virus biology.
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174
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Haider S, Yao CQ, Sabine VS, Grzadkowski M, Stimper V, Starmans MHW, Wang J, Nguyen F, Moon NC, Lin X, Drake C, Crozier CA, Brookes CL, van de Velde CJH, Hasenburg A, Kieback DG, Markopoulos CJ, Dirix LY, Seynaeve C, Rea DW, Kasprzyk A, Lambin P, Lio' P, Bartlett JMS, Boutros PC. Pathway-based subnetworks enable cross-disease biomarker discovery. Nat Commun 2018; 9:4746. [PMID: 30420699 PMCID: PMC6232113 DOI: 10.1038/s41467-018-07021-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 09/29/2018] [Indexed: 11/29/2022] Open
Abstract
Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery.
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Affiliation(s)
- Syed Haider
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada.
- Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, United Kingdom.
| | - Cindy Q Yao
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
- Diagnostic Development Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
| | - Vicky S Sabine
- Diagnostic Development Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Michal Grzadkowski
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Vincent Stimper
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Maud H W Starmans
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Jianxin Wang
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Francis Nguyen
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada
| | - Nathalie C Moon
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Xihui Lin
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Camilla Drake
- Diagnostic Development Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Cheryl A Crozier
- Diagnostic Development Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Cassandra L Brookes
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | | | | | | | | | | | | | - Daniel W Rea
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Arek Kasprzyk
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada
| | - Philippe Lambin
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Pietro Lio'
- Computer Laboratory, University of Cambridge, Cambridge, CB3 0FD, United Kingdom
| | - John M S Bartlett
- Diagnostic Development Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada.
| | - Paul C Boutros
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, M5G 1L7, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, M5S 1A8, Canada.
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175
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Kong Y, Yu T. A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data. Bioinformatics 2018; 34:3727-3737. [PMID: 29850911 PMCID: PMC6198851 DOI: 10.1093/bioinformatics/bty429] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 04/30/2018] [Accepted: 05/23/2018] [Indexed: 12/16/2022] Open
Abstract
Motivation Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This 'n≪p' property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating external gene network information could be a potential solution to this issue. Still, the problem is very challenging because (i) there are tens of thousands of features and only hundreds of training samples, (ii) the scale-free structure of the gene network is unfriendly to the setup of convolutional neural networks. Results To address these issues and build a robust classification model, we propose the Graph-Embedded Deep Feedforward Networks (GEDFN), to integrate external relational information of features into the deep neural network architecture. The method is able to achieve sparse connection between network layers to prevent overfitting. To validate the method's capability, we conducted both simulation experiments and real data analysis using a breast invasive carcinoma RNA-seq dataset and a kidney renal clear cell carcinoma RNA-seq dataset from The Cancer Genome Atlas. The resulting high classification accuracy and easily interpretable feature selection results suggest the method is a useful addition to the current graph-guided classification models and feature selection procedures. Availability and implementation The method is available at https://github.com/yunchuankong/GEDFN. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yunchuan Kong
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA
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176
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Iwata M, Hirose L, Kohara H, Liao J, Sawada R, Akiyoshi S, Tani K, Yamanishi Y. Pathway-Based Drug Repositioning for Cancers: Computational Prediction and Experimental Validation. J Med Chem 2018; 61:9583-9595. [PMID: 30371064 DOI: 10.1021/acs.jmedchem.8b01044] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.
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Affiliation(s)
- Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Fukuoka 820-8502 , Japan
| | - Lisa Hirose
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan
| | - Hiroshi Kohara
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular and Clinical Genetics, Department of Molecular Genetics, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Jiyuan Liao
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular and Clinical Genetics, Department of Molecular Genetics, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Ryusuke Sawada
- Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Sayaka Akiyoshi
- Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Kenzaburo Tani
- Project Division of ALA Advanced Medical Research, The Institute of Medical Science , The University of Tokyo , 4-6-1 Shirokanedai , Minato-ku , Tokyo 108-8639 , Japan.,Division of Molecular Design, Research Center for Systems Immunology, Medical Institute of Bioregulation , Kyushu University , 3-1-1 Maidashi , Higashi-ku , Fukuoka, Fukuoka 812-8582 , Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering , Kyushu Institute of Technology , 680-4 Kawazu , Iizuka , Fukuoka 820-8502 , Japan.,PRESTO , Japan Science and Technology Agency , Kawaguchi , Saitama 332-0012 , Japan
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177
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Tan Y, Wang Q, Xie Y, Qiao X, Zhang S, Wang Y, Yang Y, Zhang B. Identification of FOXM1 as a specific marker for triple‑negative breast cancer. Int J Oncol 2018; 54:87-97. [PMID: 30365046 PMCID: PMC6254995 DOI: 10.3892/ijo.2018.4598] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 08/30/2018] [Indexed: 12/19/2022] Open
Abstract
The present study aimed to identify the therapeutic role of the forkhead box M1 (FOXM1)-associated pathway in triple-negative breast cancer (TNBC). Using a Cancer Landscapes-based analysis, a gene regulatory network model was constructed. The present results demonstrated that FOXM1 occupies a key position in gene networks and is a critical regulatory gene in breast cancer. Using breast carcinoma gene expression data from The Cancer Genome Atlas, it was identified that FOXM1 expression was increased in the basal-like breast cancer subtype compared with other breast cancer subtypes. RNA-sequencing analysis of MDA-MB-231 cells treated with 4 and 10 µl/ml Thiostrepton identified 662 and 5,888 significantly differentially expressed genes, respectively. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses demonstrated that FOXM1 was highly associated with multiple biological processes and was markedly associated with metabolic pathways in TNBC. The use of Search Tool for the Retrieval of Interacting Genes/Proteins provided a critical assessment and integration of protein-protein interactions, and demonstrated the multiple important functions of FOXM1 in TNBC. Real-time cell analysis, reverse transcription-quantitative polymerase chain reaction and immunofluorescence staining were used to assess the anti-tumor activity of Thiostrepton in TNBC cells in vitro. The present results identified that suppression of FOXM1 using Thiostrepton inhibited MDA-MB-231 cell proliferation and the expression of cell cycle-associated genes, including cyclin A2, cyclin B2, checkpoint kinase 1, centrosomal protein 55 and polo like kinase 1. Immunofluorescence staining analysis demonstrated that vimentin, filamentous actin and zinc finger E-box-binding homeobox 1 were all decreased following treatment with Thiostrepton. Furthermore, a BALB/C nude mouse subcutaneous xenograft model was used to verify the function of FOXM1 in vivo. The present results demonstrated that FOXM1 inhibition significantly suppressed MDA-MB-231 cell tumorigenesis in vivo. Overall, the present results suggested that FOXM1 is a key gene that serves important roles in multiple biological processes in TNBC and that it may serve as a novel therapeutic target in TNBC.
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Affiliation(s)
- Yanli Tan
- Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, P.R. China
| | - Qixue Wang
- Department of Neurosurgery, Tianjin Neurological Institute, Tianjin 300052, P.R. China
| | - Yingbin Xie
- Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, P.R. China
| | - Xiaoxia Qiao
- Department of Neurosurgery, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, P.R. China
| | - Shun Zhang
- Department of Pathology, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, P.R. China
| | - Yanan Wang
- Department of Pathology, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, P.R. China
| | - Yongbin Yang
- Department of Pathology, Hebei University Medical College, Baoding, Hebei 071000, P.R. China
| | - Bo Zhang
- Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, P.R. China
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178
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Ibáñez M, Carbonell-Caballero J, Such E, García-Alonso L, Liquori A, López-Pavía M, Llop M, Alonso C, Barragán E, Gómez-Seguí I, Neef A, Hervás D, Montesinos P, Sanz G, Sanz MA, Dopazo J, Cervera J. The modular network structure of the mutational landscape of Acute Myeloid Leukemia. PLoS One 2018; 13:e0202926. [PMID: 30303964 PMCID: PMC6179200 DOI: 10.1371/journal.pone.0202926] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 08/10/2018] [Indexed: 02/06/2023] Open
Abstract
Acute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.
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Affiliation(s)
- Mariam Ibáñez
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
- Departamento de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad CEU Cardenal Herrera, Valencia, Spain
| | - José Carbonell-Caballero
- ProCURE, Catalan Institute of Oncology, Bellvitge Institute for Biomedical Research (IDIBELL), L’Hospitalet del Llobregat, Barcelona, Spain
| | - Esperanza Such
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
| | - Luz García-Alonso
- European Molecular Biology Laboratory—European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, United Kingdom
| | - Alessandro Liquori
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
| | - María López-Pavía
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Marta Llop
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
- Department of Medical Pathology, Hospital Universitario La Fe, Valencia, Spain
| | - Carmen Alonso
- Hematology Service, Hospital Arnau de Villanoba, Valencia, Spain
| | - Eva Barragán
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
- Department of Medical Pathology, Hospital Universitario La Fe, Valencia, Spain
| | - Inés Gómez-Seguí
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
| | - Alexander Neef
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | | | - Pau Montesinos
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
| | - Guillermo Sanz
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
| | - Miguel Angel Sanz
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
| | - Joaquín Dopazo
- Functional Genomics Node, Spanish National Institute of Bioinformatics at CIPF, Valencia, Spain
- Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, Sevilla, Spain
- * E-mail: (JC); (JD)
| | - José Cervera
- Hematology Service, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- Centro de Investigacion Biomédica en Red de Cáncer (CIBERONC), Instituto Carlos III, Madrid, Spain
- Genetics Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
- * E-mail: (JC); (JD)
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179
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Hao T, Wang Q, Zhao L, Wu D, Wang E, Sun J. Analyzing of Molecular Networks for Human Diseases and Drug Discovery. Curr Top Med Chem 2018; 18:1007-1014. [PMID: 30101711 PMCID: PMC6174636 DOI: 10.2174/1568026618666180813143408] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 06/22/2018] [Accepted: 07/03/2018] [Indexed: 01/11/2023]
Abstract
Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Qian Wang
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Lingxuan Zhao
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Dan Wu
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Edwin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China.,University of Calgary Cumming School of Medicine, Calgary, Alberta T2N 4Z6, Canada
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China.,Tianjin Bohai Fisheries Research Institute, Tianjin 300221, China
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180
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Chang X, Lima LDA, Liu Y, Li J, Li Q, Sleiman PMA, Hakonarson H. Common and Rare Genetic Risk Factors Converge in Protein Interaction Networks Underlying Schizophrenia. Front Genet 2018; 9:434. [PMID: 30323833 PMCID: PMC6172705 DOI: 10.3389/fgene.2018.00434] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Accepted: 09/12/2018] [Indexed: 11/25/2022] Open
Abstract
Hundreds of genomic loci have been identified with the recent advances of schizophrenia in genome-wide association studies (GWAS) and sequencing studies. However, the functional interactions among those genes remain largely unknown. We developed a network-based approach to integrate multiple genetic risk factors, which lead to the discovery of new susceptibility genes and causal sub-networks, or pathways in schizophrenia. We identified significantly and consistently over-represented pathways in the largest schizophrenia GWA studies, which are highly relevant to synaptic plasticity, neural development and signaling transduction, such as long-term potentiation, neurotrophin signaling pathway, and the ERBB signaling pathway. We also demonstrated that genes targeted by common SNPs are more likely to interact with genes harboring de novo mutations (DNMs) in the protein-protein interaction (PPI) network, suggesting a mutual interplay of both common and rare variants in schizophrenia. We further developed an edge-based search algorithm to identify the top-ranked gene modules associated with schizophrenia risk. Our results suggest that the N-methyl-D-aspartate receptor (NMDAR) interactome may play a leading role in the pathology of schizophrenia, as it is highly targeted by multiple types of genetic risk factors.
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Affiliation(s)
- Xiao Chang
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Leandro de Araujo Lima
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Yichuan Liu
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Jin Li
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Qingqin Li
- Janssen Research & Development, LLC, Titusville, NJ, United States
| | - Patrick M A Sleiman
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.,Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
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181
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Ozturk K, Dow M, Carlin DE, Bejar R, Carter H. The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine. J Mol Biol 2018; 430:2875-2899. [PMID: 29908887 PMCID: PMC6097914 DOI: 10.1016/j.jmb.2018.06.016] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/30/2018] [Accepted: 06/06/2018] [Indexed: 12/19/2022]
Abstract
Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.
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Affiliation(s)
- Kivilcim Ozturk
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Michelle Dow
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel E Carlin
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Rafael Bejar
- Moores Cancer Center, Division of Hematology and Oncology, University of California San Diego, La Jolla, CA 92093, USA
| | - Hannah Carter
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center and Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA; CIFAR, MaRS Centre, West Tower, 661 University Ave., Suite 505, Toronto, ON M5G 1M1, Canada.
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182
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Elliott A, Leicht E, Whitmore A, Reinert G, Reed-Tsochas F. A nonparametric significance test for sampled networks. Bioinformatics 2018; 34:64-71. [PMID: 29036452 PMCID: PMC5870844 DOI: 10.1093/bioinformatics/btx419] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 06/30/2017] [Indexed: 12/31/2022] Open
Abstract
Motivation Our work is motivated by an interest in constructing a protein–protein interaction network that captures key features associated with Parkinson’s disease. While there is an abundance of subnetwork construction methods available, it is often far from obvious which subnetwork is the most suitable starting point for further investigation. Results We provide a method to assess whether a subnetwork constructed from a seed list (a list of nodes known to be important in the area of interest) differs significantly from a randomly generated subnetwork. The proposed method uses a Monte Carlo approach. As different seed lists can give rise to the same subnetwork, we control for redundancy by constructing a minimal seed list as the starting point for the significance test. The null model is based on random seed lists of the same length as a minimum seed list that generates the subnetwork; in this random seed list the nodes have (approximately) the same degree distribution as the nodes in the minimum seed list. We use this null model to select subnetworks which deviate significantly from random on an appropriate set of statistics and might capture useful information for a real world protein–protein interaction network. Availability and implementation The software used in this paper are available for download at https://sites.google.com/site/elliottande/. The software is written in Python and uses the NetworkX library. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrew Elliott
- CABDyN Complexity Centre, Saïd Business School, University of Oxford, Oxford OX1 1HP, UK
| | - Elizabeth Leicht
- CABDyN Complexity Centre, Saïd Business School, University of Oxford, Oxford OX1 1HP, UK
| | | | - Gesine Reinert
- Department of Statistics, University of Oxford, Oxford, UK
| | - Felix Reed-Tsochas
- CABDyN Complexity Centre, Saïd Business School, University of Oxford, Oxford OX1 1HP, UK.,Oxford Martin School, University of Oxford, Oxford, UK
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183
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Enabling Precision Medicine through Integrative Network Models. J Mol Biol 2018; 430:2913-2923. [DOI: 10.1016/j.jmb.2018.07.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 06/15/2018] [Accepted: 07/03/2018] [Indexed: 11/17/2022]
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184
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Fröhlich H, Balling R, Beerenwinkel N, Kohlbacher O, Kumar S, Lengauer T, Maathuis MH, Moreau Y, Murphy SA, Przytycka TM, Rebhan M, Röst H, Schuppert A, Schwab M, Spang R, Stekhoven D, Sun J, Weber A, Ziemek D, Zupan B. From hype to reality: data science enabling personalized medicine. BMC Med 2018; 16:150. [PMID: 30145981 PMCID: PMC6109989 DOI: 10.1186/s12916-018-1122-7] [Citation(s) in RCA: 196] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 07/09/2018] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
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Affiliation(s)
- Holger Fröhlich
- UCB Biosciences GmbH, Alfred-Nobel-Str. Str. 10, 40789 Monheim, Germany
- University of Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19c, 53115 Bonn, Germany
| | - Rudi Balling
- University of Luxembourg, 6 avenue du Swing, 4367 Belvaux, Luxembourg
| | - Niko Beerenwinkel
- Department of Biosciences and Engineering, ETH Zurich, Mattenstr. 26, 4058 Basel, Switzerland
| | - Oliver Kohlbacher
- University of Tübingen, WSI/ZBIT, Sand 14, 72076 Tübingen, Germany
- Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, 72076 Tübingen, Germany
- Quantitative Biology Center, University of Tübingen, Auf der Morgenstelle 8, 72076 Tübingen, Germany
- Institute for Translational Bioinformatics, University Medical Center Tübingen, Sand 14, 72076 Tübingen, Germany
| | - Santosh Kumar
- Department of Computer Science, University of Memphis, 2222 Dunn Hall, Memphis, TN 38152 USA
| | - Thomas Lengauer
- Max-Planck-Institute for Informatics, 66123 Saarbrücken, Germany
| | - Marloes H. Maathuis
- ETH Zurich, Seminar für Statistik, Rämistrasse 101, 8092 Zurich, Switzerland
| | - Yves Moreau
- University of Leuven, ESAT, Kasteelpark Arenberg 10, 3001 Leuven, Belgium
| | - Susan A. Murphy
- Harvard University, Science Center 400 Suite, Oxford Street, Cambridge, MA 02138-2901 USA
| | - Teresa M. Przytycka
- National Center of Biotechnology Information, National Institute of Health, 8600 Rockville Pike, Bethesda, MD 20894-6075 USA
| | - Michael Rebhan
- Novartis Institutes for Biomedical Research, 4056 Basel, Switzerland
| | - Hannes Röst
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON M5S 3E1 Canada
| | - Andreas Schuppert
- RWTH Aachen, Joint Research Center for Computational Biomedicine, Pauwelsstrasse 19, 52074 Aachen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Aucherbachstrasse 112, 70376 Stuttgart, Germany
- University of Tübingen, Departments of Clinical Pharmacology and of Pharmacy and Biochemistry, Tübingen, Germany
| | - Rainer Spang
- University of Regensburg, Institute of Functional Genomics, Am BioPark 9, 93053 Regensburg, Germany
| | - Daniel Stekhoven
- ETH Zurich, NEXUS Personalized Health Technol., Otto-Stern-Weg 7, 8093 Zurich, Switzerland
| | - Jimeng Sun
- Georgia Tech University, 801 Atlantic Drive, Atlanta, GA 30332-0280 USA
| | - Andreas Weber
- Institute for Computer Science, University of Bonn, Endenicher Allee 19a, 53115 Bonn, Germany
| | - Daniel Ziemek
- Pfizer, Worldwide Research and Development, Linkstraße 10, 10785 Berlin, Germany
| | - Blaz Zupan
- Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia
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185
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Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations? MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2018. [DOI: 10.3390/make1010008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.
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186
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Choi J, Park S, Yoon Y, Ahn J. Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers. Bioinformatics 2018; 33:3619-3626. [PMID: 28961949 DOI: 10.1093/bioinformatics/btx487] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 07/27/2017] [Indexed: 02/07/2023] Open
Abstract
Motivation Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. Results To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. Availability and implementation https://github.com/mathcom/CPR. Contact jgahn@inu.ac.kr. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jonghwan Choi
- Department of Computer Science and Engineering, Incheon National University, Incheon, The Republic of Korea
| | - Sanghyun Park
- Department of Computer Science, Yonsei University, Seoul, The Republic of Korea
| | - Youngmi Yoon
- Department of Computer Engineering, Gachon University, Seongnam-si, Gyeonggi-do, The Republic of Korea
| | - Jaegyoon Ahn
- Department of Computer Science and Engineering, Incheon National University, Incheon, The Republic of Korea
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187
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Analyzing omics data by pair-wise feature evaluation with horizontal and vertical comparisons. J Pharm Biomed Anal 2018; 157:20-26. [PMID: 29754039 DOI: 10.1016/j.jpba.2018.04.052] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 04/29/2018] [Accepted: 04/30/2018] [Indexed: 11/24/2022]
Abstract
Feature relationships are complex and may contain important information. k top scoring pairs (k-TSP) studies feature relationships by the horizontal comparison. This study examines feature relationships and proposes vertical and horizontal k-TSP (VH-k-TSP) to identify the discriminative feature pairs by evaluating feature pairs based on the vertical and horizontal comparisons. Complexity is introduced to compute the discriminative abilities of feature pairs by means of these two comparisons. VH-k-TSP was compared with support vector machine-recursive feature elimination, relative simplicity-support vector machine, k-TSP and M-k-TSP on nine public genomics datasets. For multi-class problems, one-to-one method was used. The experiments showed that VH-k-TSP outperformed the four methods in most cases. Then, VH-k-TSP was applied to a metabolomics data of liver disease. An accuracy rate of 88.11 ± 3.30% in discrimination between cirrhosis and hepatocellular carcinoma was obtained by VH-k-TSP, better than 77.39 ± 4.10% and 79.28 ± 3.73% obtained by k-TSP and M-k-TSP, respectively. Hence combining the vertical and horizontal comparisons could define more discriminative feature pairs.
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188
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Yvinec R, Crépieux P, Reiter E, Poupon A, Clément F. Advances in computational modeling approaches of pituitary gonadotropin signaling. Expert Opin Drug Discov 2018; 13:799-813. [DOI: 10.1080/17460441.2018.1501025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Affiliation(s)
- Romain Yvinec
- PRC, INRA, CNRS, IFCE, Université de Tours, Nouzilly, France
| | | | - Eric Reiter
- PRC, INRA, CNRS, IFCE, Université de Tours, Nouzilly, France
| | - Anne Poupon
- PRC, INRA, CNRS, IFCE, Université de Tours, Nouzilly, France
| | - Frédérique Clément
- Inria, Université Paris-Saclay, Palaiseau, France
- LMS, Ecole Polytechnique, CNRS, Université Paris-Saclay, Palaiseau, France
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189
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Zheng F, Wei L, Zhao L, Ni F. Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks. BIOMED RESEARCH INTERNATIONAL 2018; 2018:5670210. [PMID: 30151386 PMCID: PMC6091292 DOI: 10.1155/2018/5670210] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 02/06/2018] [Accepted: 03/11/2018] [Indexed: 12/14/2022]
Abstract
Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective.
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Affiliation(s)
- Fang Zheng
- College of Informatics, Huazhong Agricultural University, Wuhan 430079, China
| | - Le Wei
- College of Informatics, Huazhong Agricultural University, Wuhan 430079, China
| | - Liang Zhao
- College of Informatics, Huazhong Agricultural University, Wuhan 430079, China
| | - FuChuan Ni
- College of Informatics, Huazhong Agricultural University, Wuhan 430079, China
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190
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Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles. PLoS One 2018; 13:e0201056. [PMID: 30048494 PMCID: PMC6062065 DOI: 10.1371/journal.pone.0201056] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 07/06/2018] [Indexed: 02/02/2023] Open
Abstract
The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene-gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer's disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer's disease.
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191
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Zhang C, Leng L, Zhang X, Zhao Y, Li Z. Comprehensive identification of immune-associated biomarkers based on network and mRNA expression patterns in membranous glomerulonephritis. J Transl Med 2018; 16:210. [PMID: 30041664 PMCID: PMC6056925 DOI: 10.1186/s12967-018-1586-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 07/17/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Membranous glomerulonephritis (MGN) is the most common cause of nephrotic syndrome in adult patients. Despite extensive evidences suggested that many immune-related genes could serve as effective biomarkers in MGN, the potential has not been sufficiently understood because of most previous studies have concentrated on individual gene and not the entire interaction network. METHODS Here, we integrated multiple levels of data containing immune-related genes, MGN-related genes, protein-protein interaction (PPI) networks and gene expression profiling data to construct an immune or MGN-directed neighbor network (IOMDN network) and an MGN-related genes-directed network (MGND network). RESULTS Our analysis suggested that immune-related genes in the PPI network have special topological characteristics and expression pattern related to MGN. We also identified five network modules which showed tighter network structure and stronger correlation of expression. In addition, functional and drug target analyses of genes in modules indicated that the potential mechanism for MGN. CONCLUSIONS Collectively, these results indicated that the strong associations between immune and MGN and showed the potential of immune-related genes as novel diagnostic and therapeutic targets for MGN.
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Affiliation(s)
- Chengwei Zhang
- Department of Nephrology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150006, Heilongjiang, People's Republic of China.
| | - Lei Leng
- The Second Hospital of Harbin, Heilongjiang, 150006, People's Republic of China
| | - Xiaoming Zhang
- Xiangan Hospital, The First Affiliated Hospital of Xiamen University, Xiamen, 710000, Shanxi, People's Republic of China
| | - Yao Zhao
- Department of Nephrology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150006, Heilongjiang, People's Republic of China
| | - Zhaozheng Li
- Department of Nephrology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150006, Heilongjiang, People's Republic of China
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192
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Eguchi R, Karim MB, Hu P, Sato T, Ono N, Kanaya S, Altaf-Ul-Amin M. An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease. BMC Bioinformatics 2018; 19:264. [PMID: 30005591 PMCID: PMC6043997 DOI: 10.1186/s12859-018-2251-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 06/18/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND There are different and complicated associations between genes and diseases. Finding the causal associations between genes and specific diseases is still challenging. In this work we present a method to predict novel associations of genes and pathways with inflammatory bowel disease (IBD) by integrating information of differential gene expression, protein-protein interaction and known disease genes related to IBD. RESULTS We downloaded IBD gene expression data from NCBI's Gene Expression Omnibus, performed statistical analysis to determine differentially expressed genes, collected known IBD genes from DisGeNet database, which were used to construct a IBD related PPI network with HIPPIE database. We adapted our graph-based clustering algorithm DPClusO to cluster the disease PPI network. We evaluated the statistical significance of the identified clusters in the context of determining the richness of IBD genes using Fisher's exact test and predicted novel genes related to IBD. We showed 93.8% of our predictions are correct in the context of other databases and published literatures related to IBD. CONCLUSIONS Finding disease-causing genes is necessary for developing drugs with synergistic effect targeting many genes simultaneously. Here we present an approach to identify novel disease genes and pathways and discuss our approach in the context of IBD. The approach can be generalized to find disease-associated genes for other diseases.
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Affiliation(s)
- Ryohei Eguchi
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan
| | - Mohammand Bozlul Karim
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Canada.,George and Fay Yee Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Canada.,Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada
| | - Tetsuo Sato
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan.,Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Gunma, Japan
| | - Naoaki Ono
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan
| | - Shigehiko Kanaya
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan
| | - Md Altaf-Ul-Amin
- Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan.
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193
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Jaakkola MK, McGlinchey AJ, Klén R, Elo LL. PASI: A novel pathway method to identify delicate group effects. PLoS One 2018; 13:e0199991. [PMID: 29975740 PMCID: PMC6033442 DOI: 10.1371/journal.pone.0199991] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 06/17/2018] [Indexed: 01/02/2023] Open
Abstract
Pathway analysis is a common approach in diverse biomedical studies, yet the currently-available pathway tools do not typically support the increasingly popular personalized analyses. Another weakness of the currently-available pathway methods is their inability to handle challenging data with only modest group-based effects compared to natural individual variation. In an effort to address these issues, this study presents a novel pathway method PASI (Pathway Analysis for Sample-level Information) and demonstrates its performance on complex diseases with different levels of group-based differences in gene expression. PASI is freely available as an R package.
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Affiliation(s)
- Maria K. Jaakkola
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Aidan J. McGlinchey
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Riku Klén
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L. Elo
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
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194
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Han J, Liu S, Jiang Y, Xu C, Zheng B, Jiang M, Yang H, Su F, Li C, Zhang Y. Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer. J Cell Mol Med 2018; 22:4304-4316. [PMID: 29971923 PMCID: PMC6111825 DOI: 10.1111/jcmm.13720] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 05/13/2018] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.
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Affiliation(s)
- Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Siyao Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ying Jiang
- College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Baotong Zheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Minghao Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Fei Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Harbin, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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195
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Farooqui A, Tazyeen S, Ahmed MM, Alam A, Ali S, Malik MZ, Ali S, Ishrat R. Assessment of the key regulatory genes and their Interologs for Turner Syndrome employing network approach. Sci Rep 2018; 8:10091. [PMID: 29973620 PMCID: PMC6031616 DOI: 10.1038/s41598-018-28375-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 06/15/2018] [Indexed: 12/13/2022] Open
Abstract
Turner Syndrome (TS) is a condition where several genes are affected but the molecular mechanism remains unknown. Identifying the genes that regulate the TS network is one of the main challenges in understanding its aetiology. Here, we studied the regulatory network from manually curated genes reported in the literature and identified essential proteins involved in TS. The power-law distribution analysis showed that TS network carries scale-free hierarchical fractal attributes. This organization of the network maintained the self-ruled constitution of nodes at various levels without having centrality-lethality control systems. Out of twenty-seven genes culminating into leading hubs in the network, we identified two key regulators (KRs) i.e. KDM6A and BDNF. These KRs serve as the backbone for all the network activities. Removal of KRs does not cause its breakdown, rather a change in the topological properties was observed. Since essential proteins are evolutionarily conserved, the orthologs of selected interacting proteins in C. elegans, cat and macaque monkey (lower to higher level organisms) were identified. We deciphered three important interologs i.e. KDM6A-WDR5, KDM6A-ASH2L and WDR5-ASH2L that form a triangular motif. In conclusion, these KRs and identified interologs are expected to regulate the TS network signifying their biological importance.
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Affiliation(s)
- Anam Farooqui
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Safia Tazyeen
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Mohd Murshad Ahmed
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Aftab Alam
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Shahnawaz Ali
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Md Zubbair Malik
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Sher Ali
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India.
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196
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Glaab E. Computational systems biology approaches for Parkinson's disease. Cell Tissue Res 2018; 373:91-109. [PMID: 29185073 PMCID: PMC6015628 DOI: 10.1007/s00441-017-2734-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 11/06/2017] [Indexed: 12/26/2022]
Abstract
Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.
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Affiliation(s)
- Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg.
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197
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Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-Generation Machine Learning for Biological Networks. Cell 2018; 173:1581-1592. [PMID: 29887378 DOI: 10.1016/j.cell.2018.05.015] [Citation(s) in RCA: 469] [Impact Index Per Article: 78.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 03/10/2018] [Accepted: 05/07/2018] [Indexed: 02/07/2023]
Abstract
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
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Affiliation(s)
- Diogo M Camacho
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Katherine M Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Brain & Cognitive Sciences and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rani K Powers
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - James C Costello
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
| | - James J Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Biological Engineering and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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198
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Wu M, Zhu L, Feng X. Network-based feature screening with applications to genome data. Ann Appl Stat 2018. [DOI: 10.1214/17-aoas1097] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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199
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Vafaee F, Diakos C, Kirschner MB, Reid G, Michael MZ, Horvath LG, Alinejad-Rokny H, Cheng ZJ, Kuncic Z, Clarke S. A data-driven, knowledge-based approach to biomarker discovery: application to circulating microRNA markers of colorectal cancer prognosis. NPJ Syst Biol Appl 2018; 4:20. [PMID: 29872543 PMCID: PMC5981448 DOI: 10.1038/s41540-018-0056-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 04/11/2018] [Accepted: 05/04/2018] [Indexed: 02/08/2023] Open
Abstract
Recent advances in high-throughput technologies have provided an unprecedented opportunity to identify molecular markers of disease processes. This plethora of complex-omics data has simultaneously complicated the problem of extracting meaningful molecular signatures and opened up new opportunities for more sophisticated integrative and holistic approaches. In this era, effective integration of data-driven and knowledge-based approaches for biomarker identification has been recognised as key to improving the identification of high-performance biomarkers, and necessary for translational applications. Here, we have evaluated the role of circulating microRNA as a means of predicting the prognosis of patients with colorectal cancer, which is the second leading cause of cancer-related death worldwide. We have developed a multi-objective optimisation method that effectively integrates a data-driven approach with the knowledge obtained from the microRNA-mediated regulatory network to identify robust plasma microRNA signatures which are reliable in terms of predictive power as well as functional relevance. The proposed multi-objective framework has the capacity to adjust for conflicting biomarker objectives and to incorporate heterogeneous information facilitating systems approaches to biomarker discovery. We have found a prognostic signature of colorectal cancer comprising 11 circulating microRNAs. The identified signature predicts the patients' survival outcome and targets pathways underlying colorectal cancer progression. The altered expression of the identified microRNAs was confirmed in an independent public data set of plasma samples of patients in early stage vs advanced colorectal cancer. Furthermore, the generality of the proposed method was demonstrated across three publicly available miRNA data sets associated with biomarker studies in other diseases.
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Affiliation(s)
- Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2033 Australia
| | - Connie Diakos
- Kolling Institute of Medical Research, University of Sydney, Royal North Shore Hospital, Reserve Road, St Leonards, NSW 2065 Australia
| | | | - Glen Reid
- Asbestos Diseases Research Institute, Hospital Road, Concord, NSW 2139 Australia
- Sydney Medical School, University of Sydney, Sydney, NSW 2050 Australia
| | - Michael Z. Michael
- Flinders Centre for Innovation in Cancer, Flinders Medical Centre, Flinders University, Adelaide, SA 5042 Australia
| | - Lisa G. Horvath
- Sydney Medical School, University of Sydney, Sydney, NSW 2050 Australia
- Chris O’Brien Lifehouse, Missenden Road, Camperdown, NSW 2050 Australia
- Royal Prince Alfred Hospital, Camperdown, NSW 2050 Australia
| | | | - Zhangkai Jason Cheng
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006 Australia
- School of Physics, University of Sydney, Sydney, NSW 2006 Australia
| | - Zdenka Kuncic
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006 Australia
- School of Physics, University of Sydney, Sydney, NSW 2006 Australia
| | - Stephen Clarke
- Kolling Institute of Medical Research, University of Sydney, Royal North Shore Hospital, Reserve Road, St Leonards, NSW 2065 Australia
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200
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Robinson S, Nevalainen J, Pinna G, Campalans A, Radicella JP, Guyon L. Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields. Bioinformatics 2018; 33:i170-i179. [PMID: 28881978 PMCID: PMC5870666 DOI: 10.1093/bioinformatics/btx244] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Motivation Incorporating gene interaction data into the identification of ‘hit’ genes in genomic experiments is a well-established approach leveraging the ‘guilt by association’ assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach. Results We propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Our method additionally achieves the best performance in an independent simulation experiment. The real data applications we consider comprise of a survival analysis and differential expression experiment and a cell-based RNA interference functional screen. Availability and implementation We provide all of the data and code related to the results in the paper. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sean Robinson
- CEA, BIG, Biologie à Grande Echelle, F-38054 Grenoble, France.,Université Grenoble-Alpes, F-38000 Grenoble, France.,INSERM, U1038, F-38054 Grenoble, France.,Department of Mathematics and Statistics, University of Turku, Turku, Finland.,Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland
| | - Jaakko Nevalainen
- Department of Mathematics and Statistics, University of Turku, Turku, Finland.,School of Health Sciences, University of Tampere, Tampere, Finland
| | - Guillaume Pinna
- Plateforme ARN Interférence (PArI), DSV/ISVFJ/SBIGEM/UMR 9198 I2BC, CEA Saclay, Gif-sur-Yvette, France
| | - Anna Campalans
- Institute of Molecular and Cellular Radiobiology, CEA, Fontenay-aux-Roses, France.,INSERM, U967, Fontenay-aux-Roses, France.,Université Paris Diderot, U967, Fontenay-aux-Roses, France.,Université Paris Sud, U967, Fontenay-aux-Roses, France
| | - J Pablo Radicella
- Institute of Molecular and Cellular Radiobiology, CEA, Fontenay-aux-Roses, France.,INSERM, U967, Fontenay-aux-Roses, France.,Université Paris Diderot, U967, Fontenay-aux-Roses, France.,Université Paris Sud, U967, Fontenay-aux-Roses, France
| | - Laurent Guyon
- CEA, BIG, Biologie à Grande Echelle, F-38054 Grenoble, France.,Université Grenoble-Alpes, F-38000 Grenoble, France.,INSERM, U1038, F-38054 Grenoble, France
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