Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165524
Title: Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma
Authors: Wang, Conghao
Lue, Wu
Kaalia, Rama
Kumar, Parvin
Rajapakse, Jagath Chandana
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Wang, C., Lue, W., Kaalia, R., Kumar, P. & Rajapakse, J. C. (2022). Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma. Scientific Reports, 12(1), 15425-. https://dx.doi.org/10.1038/s41598-022-19019-5
Project: AcRF Tier-1 2019-T1-002-057 
Journal: Scientific Reports 
Abstract: Multi-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis. In this paper, we evaluate two network-based approaches for integration of multi-omics data in an application of clinical outcome prediction of neuroblastoma. We derive Patient Similarity Networks (PSN) as the first step for individual omics data by computing distances among patients from omics features. The fusion of different omics can be investigated in two ways: the network-level fusion is achieved using Similarity Network Fusion algorithm for fusing the PSNs derived for individual omics types; and the feature-level fusion is achieved by fusing the network features obtained from individual PSNs. We demonstrate our methods on two high-risk neuroblastoma datasets from SEQC project and TARGET project. We propose Deep Neural Network and Machine Learning methods with Recursive Feature Elimination as the predictor of survival status of neuroblastoma patients. Our results indicate that network-level fusion outperformed feature-level fusion for integration of different omics data whereas feature-level fusion is more suitable incorporating different feature types derived from same omics type. We conclude that the network-based methods are capable of handling heterogeneity and high dimensionality well in the integration of multi-omics.
URI: https://hdl.handle.net/10356/165524
ISSN: 2045-2322
DOI: 10.1038/s41598-022-19019-5
Schools: School of Computer Science and Engineering 
Rights: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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