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

  • Yaping Fang
  • Xinkun Wang
  • Elias K. Michaelis
  • Jianwen Fang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)


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.


Aging DNA-repair Random Forest Classification Feature selection 


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  1. 1.
    Sahin, E., Depinho, R.A.: Linking functional decline of telomeres, mitochondria and stem cells during ageing. Nature 464, 520–528 (2010)CrossRefGoogle Scholar
  2. 2.
    Finkel, T., Serrano, M., Blasco, M.A.: The common biology of cancer and ageing. Nature 448, 767–774 (2007)CrossRefGoogle Scholar
  3. 3.
    Tse, M.T.: Brain ageing: a fine balance. Nat. Rev. Neurosci. 13, 222 (2012)Google Scholar
  4. 4.
    Nijnik, A., Woodbine, L., Marchetti, C., Dawson, S., Lambe, T., Liu, C., Rodrigues, N.P., Crockford, T.L., Cabuy, E., Vindigni, A., Enver, T., Bell, J.I., Slijepcevic, P., Goodnow, C.C., Jeggo, P.A., Cornall, R.J.: DNA repair is limiting for haematopoietic stem cells during ageing. Nature 447, 686–690 (2007)CrossRefGoogle Scholar
  5. 5.
    Thoms, K.M., Baesecke, J., Emmert, B., Hermann, J., Roedling, T., Laspe, P., Leibeling, D., Truemper, L., Emmert, S.: Functional DNA repair system analysis in haematopoietic progenitor cells using host cell reactivation. Scand J. Clin Lab Invest. 67, 580–588 (2007)CrossRefGoogle Scholar
  6. 6.
    Lu, T., Pan, Y., Kao, S.Y., Li, C., Kohane, I., Chan, J., Yankner, B.A.: Gene regulation and DNA damage in the ageing human brain. Nature 429, 883–891 (2004)CrossRefGoogle Scholar
  7. 7.
    Freitas, A.A., Vasieva, O., de Magalhaes, J.P.: A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related. BMC Genomics 12, 27 (2011)CrossRefGoogle Scholar
  8. 8.
    Kenyon, C.J.: The genetics of ageing. Nature 464, 504–512 (2010)CrossRefGoogle Scholar
  9. 9.
    de Magalhaes, J.P., Budovsky, A., Lehmann, G., Costa, J., Li, Y., Fraifeld, V., Church, G.M.: The Human Ageing Genomic Resources: online databases and tools for biogerontologists. Aging Cell 8, 65–72 (2009)CrossRefGoogle Scholar
  10. 10.
    Jiang, H., Ching, W.K.: Classifying DNA repair genes by kernel-based support vector machines. Bioinformation 7, 257–263 (2011)CrossRefGoogle Scholar
  11. 11.
    Fang, J.W., Dong, Y.H., Williams, T.D., Lushington, G.H.: Feature selection in validating mass spectrometry database search results. J. Bioinform Comput. Biol. 6, 223–240 (2008)CrossRefGoogle Scholar
  12. 12.
    Wang, L., Yang, M.Q., Yang, J.Y.: Prediction of DNA-binding residues from protein sequence information using random forests. BMC Genomics 10(suppl. 1), S1 (2009)Google Scholar
  13. 13.
    Sikic, M., Tomic, S., Vlahovicek, K.: Prediction of protein-protein interaction sites in sequences and 3D structures by random forests. PLoS Comput. Biol. 5, e1000278 (2009)Google Scholar
  14. 14.
    Li, Y., Fang, Y., Fang, J.: Predicting Residue-Residue Contacts Using Random Forest Models. Bioinformatics 27, 3379–3384 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wood, R.D., Mitchell, M., Sgouros, J., Lindahl, T.: Human DNA repair genes. Science 291, 1284–1289 (2001)CrossRefGoogle Scholar
  16. 16.
    Wood, R.D., Mitchell, M., Lindahl, T.: Human DNA repair genes, 2005. Mutat Res. 577, 275–283 (2005)CrossRefGoogle Scholar
  17. 17.
    Keshava Prasad, T.S., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, B., Venugopal, A., Balakrishnan, L., Marimuthu, A., Banerjee, S., Somanathan, D.S., Sebastian, A., Rani, S., Ray, S., Harrys Kishore, C.J., Kanth, S., Ahmed, M., Kashyap, M.K., Mohmood, R., Ramachandra, Y.L., Krishna, V., Rahiman, B.A., Mohan, S., Ranganathan, P., Ramabadran, S., Chaerkady, R., Pandey, A.: Human Protein Reference Database–2009 update. Nucleic Acids Research 37, D767–D772 (2009)CrossRefGoogle Scholar
  18. 18.
    Stark, C., Breitkreutz, B.J., Chatr-Aryamontri, A., Boucher, L., Oughtred, R., Livstone, M.S., Nixon, J., Van Auken, K., Wang, X., Shi, X., Reguly, T., Rust, J.M., Winter, A., Dolinski, K., Tyers, M.: The BioGRID Interaction Database: 2011 update. Nucleic Acids Research 39, D698–D704 (2011)CrossRefGoogle Scholar
  19. 19.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11 (2009)Google Scholar
  20. 20.
    Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)zbMATHCrossRefGoogle Scholar
  21. 21.
    Li, Y., Fang, J.: Distance-dependent statistical potentials for discriminating thermophilic and mesophilic proteins. Biochem Biophys Res. Commun. 396, 736–741 (2010)CrossRefGoogle Scholar
  22. 22.
    Lim, D.-S., Kim, S.-T., Xu, B., Maser, R.S., Lin, J., Petrini, J.H.J., Kastan, M.B.: ATM phosphorylates p95/nbs1 in an S-phase checkpoint pathway. Nature 404, 613–617 (2000)CrossRefGoogle Scholar
  23. 23.
    Falck, J., Coates, J., Jackson, S.P.: Conserved modes of recruitment of ATM, ATR and DNA-PKcs to sites of DNA damage. Nature 434, 605–611 (2005)CrossRefGoogle Scholar
  24. 24.
    Lombard, D.B., Chua, K.F., Mostoslavsky, R., Franco, S., Gostissa, M., Alt, F.W.: DNA repair, genome stability, and aging. Cell 120, 497–512 (2005)CrossRefGoogle Scholar
  25. 25.
    Branzei, D., Foiani, M.: Regulation of DNA repair throughout the cell cycle. Nat. Rev. Mol. Cell Biol. 9, 297–308 (2008)CrossRefGoogle Scholar
  26. 26.
    Bartek, J., Lukas, J.: DNA damage checkpoints: from initiation to recovery or adaptation. Curr. Opin. Cell Biol. 19, 238–245 (2007)CrossRefGoogle Scholar
  27. 27.
    Zlatanou, A., Despras, E., Braz-Petta, T., Boubakour-Azzouz, I., Pouvelle, C., Stewart, G.S., Nakajima, S., Yasui, A., Ishchenko, A.A., Kannouche, P.L.: The hMsh2-hMsh6 complex acts in concert with monoubiquitinated PCNA and Pol eta in response to oxidative DNA damage in human cells. Mol Cell 43, 649–662 (2011)CrossRefGoogle Scholar
  28. 28.
    Aggarwal, M., Sommers, J.A., Shoemaker, R.H., Brosh Jr., R.M.: Inhibition of helicase activity by a small molecule impairs Werner syndrome helicase (WRN) function in the cellular response to DNA damage or replication stress. Proceedings of the National Academy of Sciences of the United States of America 108, 1525–1530 (2011)CrossRefGoogle Scholar
  29. 29.
    Rodrı, X., Guez-López, A.M., Jackson, D.A., Nehlin, J.O., Iborra, F., Warren, A.V., Cox, L.S.: Characterisation of the interaction between WRN, the helicase/exonuclease defective in progeroid Werner’s syndrome, and an essential replication factor, PCNA. Mechanisms of Ageing and Development 124, 167–174 (2003)CrossRefGoogle Scholar
  30. 30.
    Chen, L., Huang, S., Lee, L., Davalos, A., Schiestl, R.H., Campisi, J., Oshima, J.: WRN, the protein deficient in Werner syndrome, plays a critical structural role in optimizing DNA repair. Aging Cell 2, 191–199 (2003)CrossRefGoogle Scholar
  31. 31.
    Hasty, P., Vijg, J.: Accelerating aging by mouse reverse genetics: a rational approach to understanding longevity. Aging Cell 3, 55–65 (2004)CrossRefGoogle Scholar
  32. 32.
    Multani, A.S., Chang, S.: WRN at telomeres: implications for aging and cancer. J. Cell Sci. 120, 713–721 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yaping Fang
    • 1
  • Xinkun Wang
    • 2
    • 3
  • Elias K. Michaelis
    • 2
    • 3
  • Jianwen Fang
    • 1
  1. 1.Applied Bioinformatics LaboratoryThe University of KansasLawrenceUSA
  2. 2.Department of Pharmacology and ToxicologyThe University of KansasLawrenceUSA
  3. 3.Higuchi Biosciences CenterThe University of KansasLawrenceUSA

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