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": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "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": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2015-02-01", 
  "url": "https://zenodo.org/record/1099834", 
  "version": "10000800", 
  "keywords": [
    "Metabolic pathways", 
    "gene expression data", 
    "microarray", 
    "Kullback\u2013Leibler divergence", 
    "KL divergence", 
    "support\nvector machines", 
    "SVM", 
    "machine learning."
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.1099834", 
  "@id": "https://doi.org/10.5281/zenodo.1099834", 
  "@type": "ScholarlyArticle", 
  "name": "Application of KL Divergence for Estimation of Each Metabolic Pathway Genes"
}
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