Skip Navigation
Skip to contents

PHRP : Osong Public Health and Research Perspectives

OPEN ACCESS
SEARCH
Search

Search

Page Path
HOME > Search
1 "Feifei Li"
Filter
Filter
Article category
Keywords
Publication year
Authors
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
  • 2,925 View
  • 16 Download
  • 7 Crossref
AbstractAbstract 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  
  • Cancer hallmark analysis using semantic classification with enhanced topic modelling on biomedical literature
    Supriya Gupta, Aakanksha Sharaff, Naresh Kumar Nagwani
    Multimedia Tools and Applications.2024;[Epub]     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
    Jnanendra Prasad Sarkar, Indrajit Saha, Adrian Lancucki, Nimisha Ghosh, Michal Wlasnowolski, Grzegorz Bokota, Ashmita Dey, Piotr Lipinski, Dariusz Plewczynski
    Frontiers in Genetics.2020;[Epub]     CrossRef
  • Class-Incremental Learning With Deep Generative Feature Replay for DNA Methylation-Based Cancer Classification
    Erdenebileg Batbaatar, Kwang Ho Park, Tsatsral Amarbayasgalan, Khishigsuren Davagdorj, Lkhagvadorj Munkhdalai, Van-Huy Pham, Keun Ho Ryu
    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

PHRP : Osong Public Health and Research Perspectives