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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    "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>", 
    "language": "eng", 
    "title": "Application of KL Divergence for Estimation of Each Metabolic Pathway Genes", 
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      "id": "CC-BY-4.0"
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    "references": [
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      "E. Hubbell, W. M. Liu, and R. Mei, \"Robust estimators for expression\nanalysis,\" Bioinformatics, vol. 18, pp. 1585-1592, 2002.\n[10] S. D. Pepper, E. K. Saunders, L. E. Edwards, C. L. Wilson, and C. J.\nMiller, \"The utility of MAS5 expression summary and detection call\nalgorithms,\" BMC Bioinformatics, vol. 8, p. 273, 2007.\n[11] M. Kanehisa, S. Goto, Y. Sato, M. Kawashima, M. Furumichi, and M.\nTanabe, \"Data, information, knowledge and principle: back to\nmetabolism in KEGG,\" Nucleic Acids Res., vol. 42, no. Database issue,\npp. D199-205, Jan. 2014."
    "keywords": [
      "Metabolic pathways", 
      "gene expression data", 
      "Kullback\u2013Leibler divergence", 
      "KL divergence", 
      "support\nvector machines", 
      "machine learning."
    "publication_date": "2015-02-01", 
    "creators": [
        "name": "Shohei Maruyama"
        "name": "Yasuo Matsuyama"
        "name": "Sachiyo Aburatani"
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