PuSH - Publication Server of Helmholtz Zentrum München

Schulte-Sasse, R.* ; Budach, S.* ; Hnisz, D.* ; Marsico, A.

Graph convolutional networks improve the prediction of cancer driver genes.

Lect. Notes Comput. Sc. 11731 LNCS, 658-668 (2019)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Despite the vast increase of high-throughput molecular data, the prediction of important disease genes and the underlying molecular mechanisms of multi-factorial diseases remains a challenging task. In this work we use a powerful deep learning classifier, based on Graph Convolutional Networks (GCNs) to tackle the task of cancer gene prediction across different cancer types. Compared to previous cancer gene prediction methods, our GCN-based model is able to combine several heterogeneous omics data types with a graph representation of the data into a single predictive model and learn abstract features from both data types. The graph formalizes relations between genes which work together in regulatory cellular pathways. GCNs outperform other state-of-the-art methods, such as network propagation algorithms and graph attention networks in the prediction of cancer genes. Furthermore, they demonstrate that including the interaction network topology greatly helps to characterize novel cancer genes, as well as entire disease modules. In this work, we go one step forward and enable the interpretation of our deep learning model to answer the following question: what is the molecular cause underlying the prediction of a disease genes and are there differences across samples?.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Cancer Gene Prediction ; Graph Convolutional Networks (gcns) ; Layer-wise Relevance Propagation (lrp) ; Multi-omics
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 11731 LNCS, Issue: , Pages: 658-668 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute of Computational Biology (ICB)