A Systematic Evaluation of Feature Selection and Classification Algorithms Using Simulated and Real miRNA Sequencing Data
作者: Sheng YangLi GuoFang ShaoYang ZhaoFeng ChenLin Lu
作者单位: 1Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Road, Nanjing, Jiangsu 211166, China
刊名: Computational and Mathematical Methods in Medicine, 2015, Vol.2015
来源数据库: Hindawi Journal
DOI: 10.1155/2015/178572
原始语种摘要: Sequencing is widely used to discover associations between microRNAs (miRNAs) and diseases. However, the negative binomial distribution (NB) and high dimensionality of data obtained using sequencing can lead to low-power results and low reproducibility. Several statistical learning algorithms have been proposed to address sequencing data, and although evaluation of these methods is essential, such studies are relatively rare. The performance of seven feature selection (FS) algorithms, including baySeq, DESeq, edgeR, the rank sum test, lasso, particle swarm optimistic decision tree, and random forest (RF), was compared by simulation under different conditions based on the difference of the mean, the dispersion parameter of the NB, and the signal to noise ratio. Real data were used to...
全文获取路径: Hindawi 

  • sequencing 排序
  • propose 提议
  • although 虽然
  • random 随机的
  • evaluate 求...的值
  • Selection 分选
  • binomial 二项式
  • dimensionality 量纲
  • widely 广泛地
  • simulation 模拟