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Classifying Aging Genes into DNA Repair or Non-DNA Repair-Related Categories

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Intelligent Computing Theories and Technology (ICIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7996))

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Abstract

The elderly population in almost every country is growing faster than ever before. However, our knowledge about the aging process is still limited despite decades of studies on this topic. In this report, we focus on the gradual accumulation of DNA damage in cells, which is a key aspect of the aging process and one that underlies age-dependent functional decline in cells, tissues, and organs. To achieve the goal of discriminating DNA-repair from non-DNA-repair genes among currently known genes related to human aging, four machine learning methods were employed: Decision Trees, Naïve Bayes, Support Vector Machine, and Random Forest (RF). Among the four methods, the RF algorithm achieved a total accuracy (ACC) of 97.32% and an area under receiver operating characteristic (AUC) of 0.98. These estimates were based on 18 selected attributes, including 10 Gene Ontology and 8 Protein-Protein Interaction (PPI) attributes. A predictive model built with only 15 PPI attributes achieved performance levels of ACC= 96.56% and AUC=0.95. Systems biology analyses showed that the features of these attributes were related to cancer, genetic, developmental, and neurological disorders, as well as DNA replication/recombination/repair, cell cycle, cell death, and cell function maintenance. The results of this study indicate that genes indicative of aging may be successfully classified into DNA repair and non-DNA repair genes and such successful classification may help identify pathways and biomarkers that are important to the aging process.

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Author information

Authors and Affiliations

  1. Applied Bioinformatics Laboratory, The University of Kansas, Lawrence, KS, 66047, USA

    Yaping Fang & Jianwen Fang

  2. Department of Pharmacology and Toxicology, The University of Kansas, Lawrence, KS, 66047, USA

    Xinkun Wang & Elias K. Michaelis

  3. Higuchi Biosciences Center, The University of Kansas, Lawrence, KS, 66047, USA

    Xinkun Wang & Elias K. Michaelis

Authors
  1. Yaping Fang
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  2. Xinkun Wang
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  3. Elias K. Michaelis
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  4. Jianwen Fang
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, 680-749 #7-413, San 29, Muger Dong, Ulsan, South Korea

    Kang-Hyun Jo

  3. Guangxi University for Nationalities, 530006, Nanning, Guangxi, China

    Yong-Quan Zhou

  4. School of Computer Science and Engineering, Inha University, 402-751, Incheon, South Korea

    Kyungsook Han

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Fang, Y., Wang, X., Michaelis, E.K., Fang, J. (2013). Classifying Aging Genes into DNA Repair or Non-DNA Repair-Related Categories. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-39482-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39481-2

  • Online ISBN: 978-3-642-39482-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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