SciELO - Scientific Electronic Library Online

 
vol.31 issue4Modulation of the norfloxacin resistance in Staphylococcus aureus by Croton campestris A. and Ocimum gratissimum L author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google

Share


Biomédica

Print version ISSN 0120-4157On-line version ISSN 2590-7379

Abstract

CIFUENTES, Ricardo A  and  BARRETO, Emiliano. Supervised selection of single nucleotide polymorphisms in chronic fatigue síndrome. Biomédica [online]. 2011, vol.31, n.4, pp.613-621. ISSN 0120-4157.

Introduction: The different ways for selecting single nucleotide polymorphisms have been related to paradoxical conclusions about their usefulness in predicting chronic fatigue syndrome even when using the same dataset. Objective: To evaluate the efficacy in predicting this syndrome by using polymorphisms selected by a supervised approach that is claimed to be a method that helps identifying their optimal profile. Materials and methods: We eliminated those polymorphisms that did not meet the Hardy-Weinberg equilibrium. Next, the profile of polymorphisms was obtained through the supervised approach and three aspects were evaluated: comparison of prediction accuracy with the accuracy of a profile that was based on linkage disequilibrium, assessment of the efficacy in determining a higher risk stratum, and estimating the algorithm influence on accuracy. Results: A valid profile (p<0.01) was obtained with a higher accuracy than the one based on linkage disequilibrium, 72.8 vs. 62.2% (p<0.01). This profile included two known polymorphisms associated with chronic fatigue syndrome, the NR3C1_11159943 major allele and the 5HTT_7911132 minor allele. Muscular pain or sinus nasal symptoms in the stratum with the profile predicted V with a higher accuracy than those symptoms in the entire dataset, 87.1 vs. 70.4% (p<0.01) and 92.5 vs. 71.8% (p<0.01) respectively. The profile led to similar accuracies with different algorithms. Conclusions: The supervised approach made it possible to discover a reliable profile of polymorphisms associated with this syndrome. Using this profile, accuracy for this dataset was the highest reported and it increased when the profile was combined with clinical data.

Keywords : genetic polymorphism; chronic fatigue syndrome; computational biology; artificial intelligence; systems biology; linkage disequilibrium.

        · abstract in Spanish     · text in English     · English ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License