Journal article Open Access

Application of KL Divergence for Estimation of Each Metabolic Pathway Genes

Shohei Maruyama; Yasuo Matsuyama; Sachiyo Aburatani

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  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  "description": "<p>Development of a method to estimate gene functions is<br>\nan important task in bioinformatics. One of the approaches for the<br>\nannotation is the identification of the metabolic pathway that genes are<br>\ninvolved in. Since gene expression data reflect various intracellular<br>\nphenomena, those data are considered to be related with genes&rsquo;<br>\nfunctions. However, it has been difficult to estimate the gene function<br>\nwith high accuracy. It is considered that the low accuracy of the<br>\nestimation is caused by the difficulty of accurately measuring a gene<br>\nexpression. Even though they are measured under the same condition,<br>\nthe gene expressions will vary usually. In this study, we proposed a<br>\nfeature extraction method focusing on the variability of gene<br>\nexpressions to estimate the genes&#39; metabolic pathway accurately. First,<br>\nwe estimated the distribution of each gene expression from replicate<br>\ndata. Next, we calculated the similarity between all gene pairs by KL<br>\ndivergence, which is a method for calculating the similarity between<br>\ndistributions. Finally, we utilized the similarity vectors as feature<br>\nvectors and trained the multiclass SVM for identifying the genes&#39;<br>\nmetabolic pathway. To evaluate our developed method, we applied the<br>\nmethod to budding yeast and trained the multiclass SVM for<br>\nidentifying the seven metabolic pathways. As a result, the accuracy<br>\nthat calculated by our developed method was higher than the one that<br>\ncalculated from the raw gene expression data. Thus, our developed<br>\nmethod combined with KL divergence is useful for identifying the<br>\ngenes&#39; metabolic pathway.</p>", 
  "license": "", 
  "creator": [
      "@type": "Person", 
      "name": "Shohei Maruyama"
      "@type": "Person", 
      "name": "Yasuo Matsuyama"
      "@type": "Person", 
      "name": "Sachiyo Aburatani"
  "headline": "Application of KL Divergence for Estimation of Each Metabolic Pathway Genes", 
  "image": "", 
  "datePublished": "2015-02-01", 
  "url": "", 
  "version": "10000800", 
  "keywords": [
    "Metabolic pathways", 
    "gene expression data", 
    "Kullback\u2013Leibler divergence", 
    "KL divergence", 
    "support\nvector machines", 
    "machine learning."
  "@context": "", 
  "identifier": "", 
  "@id": "", 
  "@type": "ScholarlyArticle", 
  "name": "Application of KL Divergence for Estimation of Each Metabolic Pathway Genes"
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