Part of the Advanced Information and Knowledge Processing book series (AI&KP)


Data mining (or machine learning) techniques have attracted considerable attention from both academia and industry, due to their significant contributions to intelligent data analysis. The importance of data mining and its applications is likely to increase even further in the future, given that organisations keep collecting increasingly larger amounts of data and more diverse types of data. Due to the rapid growth of data from real world applications, it is timely to adopt Knowledge Discovery in Databases (KDD) methods to extract knowledge or valuable information from data. Indeed, KDD has already been successfully adopted in real world applications, both in science and in business.


  1. 1.
    de Magalhães JP, Budovsky A, Lehmann G, Costa J, Li Y, Fraifeld V, Church GM (2009) The human ageing genomic resources: online databases and tools for biogerontologists. Aging Cell 8(1):65–72CrossRefGoogle Scholar
  2. 2.
    Fang Y, Wang X, Michaelis EK, 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) Lecture notes in intelligent computing theories and technology. Springer, Berlin, pp 20–29CrossRefGoogle Scholar
  3. 3.
    Fellbaum C (1998) WordNet. Blackwell Publishing Ltd, HobokenGoogle Scholar
  4. 4.
    Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, BerlinGoogle Scholar
  5. 5.
    Freitas AA, de Magalhães JP (2011) A review and appraisal of the DNA damage theory of ageing. Mutat Res 728(1–2):12–22CrossRefGoogle Scholar
  6. 6.
    Freitas AA, Vasieva O, de Magalhães JP (2011) A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related. BMC Genomics 12(27):1–11 JanGoogle Scholar
  7. 7.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182Google Scholar
  8. 8.
    Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques: concepts and techniques. Elsevier, San FranciscoGoogle Scholar
  9. 9.
    Huang T, Zhang J, Xu ZP, Hu LL, Chen L, Shao JL, Zhang L, Kong XY, Cai YD, Chou KC (2012) Deciphering the effects of gene deletion on yeast longevity using network and machine learning approaches. Biochimie 94(4):1017–1025CrossRefGoogle Scholar
  10. 10.
    Kenyon CJ (2010) The genetics of ageing. Nature 464(7288):504–512CrossRefGoogle Scholar
  11. 11.
    Li YH, Dong MQ, Guo Z (2010) Systematic analysis and prediction of longevity genes in caenorhabditis elegans. Mech Ageing Dev 131(11–12):700–709CrossRefGoogle Scholar
  12. 12.
    Liu H, Motoda H (1998) Feature extraction, construction and selection: a data mining perspective. Springer, USGoogle Scholar
  13. 13.
    Masoro EJ (2005) Overview of caloric restriction and ageing. Mech Ageing Dev 126(9):913–922CrossRefGoogle Scholar
  14. 14.
    Miller GA, Beckwith R, Fellbaum C, Gross D, Miller KJ . Introduction to wordnet: an on-line lexical database. Int J Lexicogr 3(4):235–244CrossRefGoogle Scholar
  15. 15.
    Pereira RB, Plastino A, Zadrozny B, de C Merschmann LH, Freitas AA, (2011) Lazy attribute selection: choosing attributes at classification time. Intell Data Anal 15(5):715–732CrossRefGoogle Scholar
  16. 16.
    Tacutu R, Craig T, Budovsky A, Wuttke D, Lehmann G, Taranukha D, Costa J, Fraifeld VE, de Magalhães JP (2013) Human ageing genomic resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res 41(D1):D1027–D1033CrossRefGoogle Scholar
  17. 17.
    The Gene Ontology Consortium (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–29CrossRefGoogle Scholar
  18. 18.
    Wan C, Freitas AA (2013) Prediction of the pro-longevity or anti-longevity effect of Caenorhabditis Elegans genes based on Bayesian classification methods., pp 373–380Google Scholar
  19. 19.
    Wan C, Freitas AA (2015) Two methods for constructing a gene ontology-based feature selection network for a Bayesian network classifier and applications to datasets of aging-related genes., pp 27–36Google Scholar
  20. 20.
    Wan C, Freitas AA, de Magalhães JP (2015) Predicting the pro-longevity or anti-longevity effect of model organism genes with new hierarchical feature selection methods. IEEE/ACM Trans Comput Biol Bioinform 12(2):262–275Google Scholar
  21. 21.
    Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, BurlingtonCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

Personalised recommendations