Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86070
Title: Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach
Authors: Rajapakse, Jagath Chandana
Azuaje, Francisco
Tranchevent, Léon-Charles
Nazarov, Petr V.
Kaoma, Tony
Schmartz, Georges P.
Muller, Arnaud
Kim, Sang-Yoon
Keywords: Biological Networks
Network-based Methods
Issue Date: 2018
Source: Tranchevent, L.-C., Nazarov, P. V., Kaoma, T., Schmartz, G. P., Muller, A., Kim, S.-Y., et al. (2018). Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach. Biology Direct, 13(1), 12- .
Series/Report no.: Biology Direct
Abstract: Background: One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. Results: We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. Conclusion: We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data.
URI: https://hdl.handle.net/10356/86070
http://hdl.handle.net/10220/45269
ISSN: 1745-6150
DOI: 10.1186/s13062-018-0214-9
Schools: School of Computer Science and Engineering 
Research Centres: Bioinformatics Research Centre 
Rights: © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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