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首页> 外文会议>Distributed computing and artificial intelligence. >A Hybrid of SVM and SCAD with Group-Specific Tuning Parameter for Pathway-Based Microarray Analysis
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A Hybrid of SVM and SCAD with Group-Specific Tuning Parameter for Pathway-Based Microarray Analysis

机译：SVM和SCAD的混合，具有基于组的优化参数，用于基于路径的微阵列分析

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摘要

The incorporation of pathway data into the microarray analysis had lead to a new era in advance understanding of biological processes. However, this advancement is limited by the two issues in quality of pathway data. First, the pathway data are usually made from the biological context free, when it comes to a specific cellular process (e.g. lung cancer development), it can be that only several genes within pathways are responsible for the corresponding cellular process. Second, pathway data commonly curated from the literatures, it can be that some pathway may be included with the uninformative genes while the informative genes may be excluded. In this paper, we proposed a hybrid of support vector machine and smoothly clipped absolute deviation with group-specific tuning parameters (gSVM-SCAD) to select informative genes within pathways before the pathway evaluation process. Our experiments on lung cancer and gender data sets show that gSVM-SCAD obtains significant results in classification accuracy and in selecting the informative genes and pathways.
机译：将途径数据整合到微阵列分析中已经进入了一个预先了解生物学过程的新时代。但是，这种进展受到途径数据质量中两个问题的限制。首先，途径数据通常是不受生物学影响的，当涉及到特定的细胞过程（例如肺癌发展）时，可能是途径中只有几个基因负责相应的细胞过程。第二，通常从文献中收集的途径数据，可能是某些途径可能包含在非信息基因中，而信息基因可能被排除在外。在本文中，我们提出了一种混合支持向量机，并使用特定于组的调整参数（gSVM-SCAD）平滑修剪绝对偏差，以在路径评估过程之前选择路径内的信息基因。我们在肺癌和性别数据集上的实验表明，gSVM-SCAD在分类准确性以及选择信息性基因和途径方面获得了显著成果。

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  • 来源
  • 会议地点 Salamanca(ES);Salamanca(ES)
  • 作者

    Muhammad Faiz Misman; Mohd Saberi Mohamad; Safaai Deris; Raja Nurul Mardhiah Raja Mohamad; Siti Zaiton Mohd Hashim; Sigeru Omatu;

  • 作者单位

    Artificial Intelligence Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johor, Malaysia;

    Artificial Intelligence Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johor, Malaysia;

    Artificial Intelligence Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johor, Malaysia;

    Artificial Intelligence Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johor, Malaysia;

    Soft Computing Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310, Skudai, Johor Darul Takzim, Malaysia;

    Department of Electronics, Information and Communication Engineering,Osaka Institute of Technology, Osaka 535-8585, Japan;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人工智能理论;人工智能理论;
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