CyNetSVM: A cytoscape App for cancer biomarker identification using network constrained support vector machines

Xu Shi, Sharmi Banerjee, Li Chen, Leena Hilakivi-Clarke, Robert Clarke, Jianhua Xuan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and; a sample data set is also provided at

Original languageEnglish (US)
Article numbere0170482
JournalPloS one
Issue number1
StatePublished - Jan 2017
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health (Grant numbers: CA149653, CA164384, CA149147 and CA184902); URL: http:// The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publication of this article was supported by Virginia Tech's Open Access Subvention Fund.

Publisher Copyright:
© 2017 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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