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Original Article
- A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs
- Feifei Li, Minghao Piao, Yongjun Piao, Meijing Li, Keun Ho Ryu
- Osong Public Health Res Perspect. 2014;5(5):279-285. Published online October 31, 2014
- DOI: https://doi.org/10.1016/j.phrp.2014.08.004
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- 17 Download
- 7 Crossref
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Abstract
PDF - Objectives
Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification.
Methods
We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification.
Results
The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods.
Conclusion
Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification. -
Citations
Citations to this article as recorded by
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Multimedia Tools and Applications.2024; 83(31): 76429. CrossRef - Multi-Task Topic Analysis Framework for Hallmarks of Cancer with Weak Supervision
Erdenebileg Batbaatar, Van-Huy Pham, Keun Ho Ryu
Applied Sciences.2020; 10(3): 834. CrossRef - Microarray cancer feature selection: Review, challenges and research directions
Moshood A. Hambali, Tinuke O. Oladele, Kayode S. Adewole
International Journal of Cognitive Computing in En.2020; 1: 78. CrossRef - Identification of miRNA Biomarkers for Diverse Cancer Types Using Statistical Learning Methods at the Whole-Genome Scale
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Frontiers in Genetics.2020;[Epub] CrossRef - Class-Incremental Learning With Deep Generative Feature Replay for DNA Methylation-Based Cancer Classification
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IEEE Access.2020; 8: 210800. CrossRef - MicroRNA-449a enhances radiosensitivity by downregulation of c-Myc in prostate cancer cells
Aihong Mao, Qiuyue Zhao, Xin Zhou, Chao Sun, Jing Si, Rong Zhou, Lu Gan, Hong Zhang
Scientific Reports.2016;[Epub] CrossRef - Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data
Peipei Li, Yongjun Piao, Ho Sun Shon, Keun Ho Ryu
BMC Bioinformatics.2015;[Epub] CrossRef
- Cancer hallmark analysis using semantic classification with enhanced topic modelling on biomedical literature
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