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Enhancement of COPD biological networks using a web-based collaboration interface.

sbv IMPROVER project team (in alphabetical order), and Boue, Stephanie and Fields, Brett and Hoeng, Julia and Park, Jennifer and Peitsch, Manuel C and Schlage, Walter K and Talikka, Marja and Challenge Best Performers (in alphabetical order), and Binenbaum, Ilona and Bondarenko, Vladimir and Bulgakov, Oleg V and Cherkasova, Vera and Diaz-Diaz, Norberto and Fedorova, Larisa and Guryanova, Svetlana and Guzova, Julia and Igorevna Koroleva, Galina and Kozhemyakina, Elena and Kumar, Rahul and Lavid, Noa and Lu, Qingxian and Menon, Swapna and Ouliel, Yael and Peterson, Samantha C and Prokhorov, Alexander and Sanders, Edward and Schrier, Sarah and Schwaitzer Neta, Golan and Shvydchenko, Irina and Tallam, Aravind and Villa-Fombuena, Gema and Wu, John and Yudkevich, Ilya and Zelikman, Mariya (2015) Enhancement of COPD biological networks using a web-based collaboration interface. F1000Res, 4. 32 - ?. ISSN 2046-1402

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Abstract

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
sbv IMPROVER project team (in alphabetical order),
Boue, Stephanie
Fields, Brett
Hoeng, Julia
Park, Jennifer
Peitsch, Manuel C
Schlage, Walter K
Talikka, Marja
Challenge Best Performers (in alphabetical order),
Binenbaum, Ilona
Bondarenko, Vladimir
Bulgakov, Oleg V
Cherkasova, Vera
Diaz-Diaz, Norberto
Fedorova, Larisa
Guryanova, Svetlana
Guzova, Julia
Igorevna Koroleva, Galina
Kozhemyakina, Elena
Kumar, Rahul
Lavid, Noa
Lu, Qingxian
Menon, Swapna
Ouliel, Yael
Peterson, Samantha C
Prokhorov, Alexander
Sanders, Edward
Schrier, Sarah
Schwaitzer Neta, Golan
Shvydchenko, Irina
Tallam, Aravind
Villa-Fombuena, Gema
Wu, John
Yudkevich, Ilya
Zelikman, Mariya
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
ReviewerHide, WinstonUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
ReviewerBader, GaryUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
ReviewerRuch, PatrickUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date: 14 May 2015
Date Type: Acceptance
Journal or Publication Title: F1000Res
Volume: 4
Page Range: 32 - ?
DOI or Unique Handle: 10.12688/f1000research.5984.2
Refereed: Yes
Uncontrolled Keywords: COPD, Chronic Obstructive Pulmonary Disease, crowd verification, crowdsourcing, jamboree, network model, online collaboration, signaling pathway
ISSN: 2046-1402
Date Deposited: 08 Aug 2016 17:38
Last Modified: 13 Oct 2017 21:58
URI: http://d-scholarship.pitt.edu/id/eprint/29135

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