一种融合T-Rank和Softmax的特征提取算法研究
The Research of Feature Extraction Algorithm by Integrating T-Rank and Softmax Methods

作者: 刘哲 , 陈鹏 :湖北工业大学电气与电子工程学院,湖北 武汉; 彭春力 :湖北工业大学经济与管理学院,湖北 武汉; 罗幼喜 :湖北工业大学理学院,湖北 武汉;

关键词: 高维Softmax算法T-Rank算法银屑病基因表达谱High Dimensional Softmax Algorithm T-Rank Algorithm Psoriasis Gene Expression

摘要: 本文针对高维生物数据特征提出了一种融合T-Rank和Softmax的特征提取算法。该方法比传统特征提取方法在处理高维生物数据更加有效,不仅提取的特征个数较少,而且计算速度快。利用算法本文对高维银屑病基因表达谱数据进行了研究,得到了分类准确率较高的疾病诊断模型。

Abstract: The paper proposed a new feature extraction algorithm by integrating T-rank and Softmax for the high dimensional biological data sets, which is more effective than traditional method when dealing with high dimensional data. It can not only extract a very few number of features, but also have fast computing speed. By using of this new algorithm, the paper obtains a high accuracy diagnosis model for psoriasis.

文章引用: 刘哲 , 彭春力 , 陈鹏 , 罗幼喜 (2016) 一种融合T-Rank和Softmax的特征提取算法研究。 建模与仿真, 5, 123-130. doi: 10.12677/MOS.2016.54017

参考文献

[1] 邹晶, 高磊, 李晋, 戴静珠, 李霞. 针对不同特征基因挖掘方法的特征基因功能一致性分析[J]. 中国生物医学工程学报, 2010, 29(2): 212-213.

[2] 李霞, 张田文, 郭政. 一种基于递归分类树的集成特征基因选择方法[J]. 计算机学报, 2004, 27(5): 675-682.

[3] 李颖新, 阮晓钢. 基于支持向量机的肿瘤分类特征基因基因选取[J]. 计算机研究与发展, 2005, 42(10): 1796- 1801.

[4] 吕飒丽, 汪强虎, 李霞, 郭政. 基于决策森林特征基因的两种识别方法[J]. 生物信息学, 2004, 2(3): 19-22.

[5] 张飞, 王世祥, 王玲, 宋凯. 肺鳞状细胞癌癌症发展模式识别分类模型及特征基因识[J]. 生物化学与生物物理进展, 2016, 43(1): 63-74.

[6] Villasenor-Park, J., Wheeler, D. and Grandinetti, L. (2012) Psoriasis: Evolving Treatment for a Complex Disease. Cleveland Clinic Journal of Medicine, 79, 413-423.
http://dx.doi.org/10.3949/ccjm.79a.11133

[7] Yao, Y., et al. (2008) Type I Interferon: Potential Therapeutic Target for Psoriasis? PLoS ONE, 3, e2737.
http://dx.doi.org/10.1371/journal.pone.0002737

[8] Swindell, W.R., et al. (2011) Genome-Wide Expression Profiling of Five Mouse Models Identifies Similarities and Differences with Human Psoriasis. PLoS ONE, 6, e18266.
http://dx.doi.org/10.1371/journal.pone.0018266

[9] Nair, R.P., et al. (2009) Genome-Wide Scan Reveals Association of Pso-riasis with IL-23 and NF-KappaB Pathways. Nature Genetics, 41, 199-204.
http://dx.doi.org/10.1038/ng.311

[10] Barrett, T., et al. (2011) NCBI GEO: Archive for Functional Genomics Data Sets—10 Years on. Nucleic Acids Research, 39, D1005-D1010.
http://dx.doi.org/10.1093/nar/gkq1184

[11] Irizarry, R.A., et al. (2003) Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics, 4, 249-264.
http://dx.doi.org/10.1093/biostatistics/4.2.249

[12] Benito, M., et al. (2004) Adjustment of Systematic Microarray Data Biases. Bioinformatics, 20, 105-114.
http://dx.doi.org/10.1093/bioinformatics/btg385

分享
Top