Reference : A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
Systems Biomedicine
A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
Tranchevent, Leon-Charles mailto [Luxembourg Institute of Health- LIH > Department of Oncology > Proteome and Genome Research Unit > ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science (Glaab Group)]
Azuaje, Francisco [Luxembourg Institute of Health - LIH > Department of Oncology > Proteome and Genome Research Unit > ; UCB Celltech > Data and Translational Sciences]
Rajapakse, Jagath [Nanyang Technological University > School of Computer Science and Engineering > Bioinformatics Research Center]
BMC Medical Genomics
BioMed Central
Yes (verified by ORBilu)
United Kingdom
[en] Machine learning ; Deep learning ; Deep neural network ; Network-based methods ; Graph topology ; Disease prediction ; Clinical outcome prediction
[en] Background
The availability of high-throughput omics datasets from large patient cohorts has allowed the development of methods that aim at predicting patient clinical outcomes, such as survival and disease recurrence. Such methods are also important to better understand the biological mechanisms underlying disease etiology and development, as well as treatment responses. Recently, different predictive models, relying on distinct algorithms (including Support Vector Machines and Random Forests) have been investigated. In this context, deep learning strategies are of special interest due to their demonstrated superior performance over a wide range of problems and datasets. One of the main challenges of such strategies is the “small n large p” problem. Indeed, omics datasets typically consist of small numbers of samples and large numbers of features relative to typical deep learning datasets. Neural networks usually tackle this problem through feature selection or by including additional constraints during the learning process.

We propose to tackle this problem with a novel strategy that relies on a graph-based method for feature extraction, coupled with a deep neural network for clinical outcome prediction. The omics data are first represented as graphs whose nodes represent patients, and edges represent correlations between the patients’ omics profiles. Topological features, such as centralities, are then extracted from these graphs for every node. Lastly, these features are used as input to train and test various classifiers.

We apply this strategy to four neuroblastoma datasets and observe that models based on neural networks are more accurate than state of the art models (DNN: 85%-87%, SVM/RF: 75%-82%). We explore how different parameters and configurations are selected in order to overcome the effects of the small data problem as well as the curse of dimensionality.

Our results indicate that the deep neural networks capture complex features in the data that help predicting patient clinical outcomes.
Fonds National de la Recherche (FNR), Luxembourg (SINGALUN project) ; Tier-2 grant MOE2016-T2-1-029 by the Ministry of Education, Singapore
Researchers ; Students
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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 ( applies to the data made available in this article, unless otherwise stated.
FnR ; FNR11651464 > Enrico Glaab > PD-Strat > Multi-dimensional stratification of Parkinson’s disease patients for personalised interventions > 01/07/2018 > 30/06/2021 > 2018

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