Application of network diffusion approaches to drug screenings: A perspective on multilayered networks derived from cell lines and drugs
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Abstract
Network diffusion approaches are frequently used for identifying the relevant disease genes and for prioritizing the genes for drug sensitivity predictions. Majority of these studies rely on networks representing a single type of information. However, using multiplex heterogeneous networks (networks with multiple interconnected layers) is much more informative and helps to understand the global topology. We built a multi-layered network that incorporates information on protein-protein interactions, drug-drug similarities, cell line-cell line similarities and co-expressed genes. We applied Random Walk with Restart algorithm to investigate the interactions between drugs, targets and cancer cell lines. Results of ANOVA models show that these prioritized genes are among the most significant ones that relate to drug response. Moreover, the predictive power of the drug response prediction models built using the gene expression data of only the top ranked genes is similar to the models built using all the available genes. Taken together, the results confirm that the multiplex heterogeneous network-based approach is efficient in identifying the most significant genes associated with drug response.
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2017. Application of network diffusion approaches to drug screenings: A perspective on multilayered networks derived from cell lines and drugs. PeerJ Preprints 5:e3339v1 https://doi.org/10.7287/peerj.preprints.3339v1Author comment
This is an abstract which has been accepted for the NETTAB 2017 Workshop.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Vigneshwari Subramanian conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Bence Szalai conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.
Luis Tobalina contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.
Julio Saez-Rodriguez conceived and designed the experiments, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
The research in this article did not generate any data or code.
Funding
PrECISE (Personalized Engine for Cancer Integrative Study and Evaluation) is a European H2020 project focused on prostate cancer, an international collaboration that aims to translate into clinical advances many of the technical and methodological developments achieved during the last years. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668858. PrECISE is supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reflect the official views of the Swiss Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
