Heterogeneous networks integration for disease–gene prioritization with node kernels
| Autor: | Tran, V. D., Sperduti, A., Backofen, R., Costa, F. |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Disease gene Prioritization Computer science Gene regulatory network Proteins Reproducibility of Results computer.software_genre Biochemistry Partition (database) Graph Computer Science Applications Computational Mathematics Computational Theory and Mathematics Discriminative model Humans Gene Regulatory Networks Software Data mining Molecular Biology computer Heterogeneous network |
| Zdroj: | Bioinformatics. 36:2649-2656 |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/btaa008 |
| Popis: | Motivation The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems. Results We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease–gene associations and on a time-stamped benchmark containing 42 newly discovered associations. Availability and implementation Source code: https://github.com/dinhinfotech/DiGI.git. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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