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. 2017 Jan 25;12(1):e0170482.
doi: 10.1371/journal.pone.0170482. eCollection 2017.

CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines

Xu Shi  1 , Sharmi Banerjee  1 , Li Chen  2 , Leena Hilakivi-Clarke  3 , Robert Clarke  3 , Jianhua Xuan  1
Affiliations

CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines

Xu Shi et al. PLoS One. .

Abstract

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 http://sourceforge.net/projects/netsvmjava; a sample data set is also provided at sourceforge.net.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. An overview of the CyNetSVM app.
Fig 2
Fig 2. Screenshot of the CyNetSVM app.
Fig 3
Fig 3. Network identified from Loi et al. data.
Fig 4
Fig 4. ROC curve of the classification of patients in Loi et al. data.
Fig 5
Fig 5. Network identified from METABRIC discovery data.
Fig 6
Fig 6. ROC curve of the classification of patients in METABRIC validation data.
See this image and copyright information in PMC

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