Network-based support vector machine for classification of microarray samples

By Yanni Zhu, Xiaotong Shen, Wei Pan. 2009

120 citations recorded by the source corpus.

BackgroundThe importance of network-based approach to identifying biological markers for diagnostic classification and prognostic assessment in the context of microarray data has been increasingly recognized. To our knowledge, there have been few, if any, statistical tools that explicitly incorporate the prior information of gene networks into classifier building. The main idea of this paper is to take full advantage of the biological observation that neighboring genes in a network tend to function together in biological processes and to embed this information into a formal statistical framework.ResultsWe propose a network-based support vector machine for binary classification problems by constructing a penalty term from the F∞-norm being applied to pairwise gene neighbors with the hope to improve predictive performance and gene selection. Simulation studies in both low- and high-dimensional data settings as well as two real microarray applications indicate that the proposed method is able to identify more clinically relevant genes while maintaining a sparse model with either similar or higher prediction accuracy compared with the standard and the L1 penalized support vector machines.ConclusionThe proposed network-based support vector machine has the potential to be a practically useful classification tool for microarrays and other high-dimensional data.

Subfields: Molecular Biology

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In Molecular Biology

1. Julio César Díaz Vera
2. Xin Lai
3. Zhaohui S. Qin
4. Yujie You
5. Le Zhang
6. Suran Liu
7. L. Huang
8. Ying Pan

Citations in the corpus

• EgoNet: identification of human disease ego-network modules (2014, methodology): “[21] applied network-based Support Vector Machine (SVM) for classification of microarray samples but the method only worked for small subnetworks.”
• Artificial intelligence in cancer target identification and drug discovery (2022, background): “These ML-based biology analysis applications for novel anticancer targets identi fi cation consist of classi fi cation 201 , clustering 202 , neural networks 203,204 , and so on 205 .”
• Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction (2019, methodology): “Di ff erent from extracting the network features directly, Zhu et al. [6] constructed an SVM classifier based on gene expression data directly to classify the status of Parkinson’s patients by taking network data as a punishment term.”