A comparison of classification methods for predicting chronic fatigue syndrome based on genetic data

Journal of Translational Medicine

Volumn 7, Issue , 2009, Pages 81-

  Huang, Lung Cheng ^a,b   Hsu, Sen Yen ^c   Lin, Eugene ^d  

^a NATIONAL TAIWAN UNIVERSITY HOSPITAL   (Taiwan)

^b KAOHSIUNG MEDICAL UNIVERSITY   (Taiwan)

^c CHI MEI MEDICAL CENTER   (Taiwan)

^d VITA GENOMICS INC   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; BAYES THEOREM; CHRONIC FATIGUE SYNDROME; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DECISION TREE; DISEASE PREDISPOSITION; FEMALE; GENETIC ASSOCIATION; HEREDITY; HUMAN; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MALE; PREDICTION; SENSITIVITY AND SPECIFICITY; SINGLE NUCLEOTIDE POLYMORPHISM; SUPPORT VECTOR MACHINE; ALGORITHM; BIOLOGY; CLASSIFICATION; GENETIC PREDISPOSITION; GENETICS; GENOMICS; METHODOLOGY; REPRODUCIBILITY;

ALGORITHMS; BAYES THEOREM; COMPUTATIONAL BIOLOGY; DECISION TREES; FATIGUE SYNDROME, CHRONIC; GENETIC PREDISPOSITION TO DISEASE; GENOMICS; HUMANS; POLYMORPHISM, SINGLE NUCLEOTIDE; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 73349119975     PISSN: None     EISSN: 14795876     Source Type: Journal    
DOI: 10.1186/1479-5876-7-81     Document Type: Article

References (38)

1. Griffith JP, Zarrouf FA: A systematic review of chronic fatigue syndrome: don't assume it's depression. Prim Care Companion J Clin sychiatry 2008, 10:120-128.
2. Fukuda K, Straus SE, Hickie I, Sharpe MC, Obbins JG, Komaroff A: The chronic fatigue syndrome: a comprehensive approach to its efinition and study. Ann Intern Med 1994, 121:953-959.
3. Afari N, Buchwald D: Chronic fatigue yndrome: a review. Am J Psychiatry 2003, 160:221-236.
4. Reeves WC, Wagner D, Nisenbaum R, Jones JF Gurbaxani B, Solomon L, Papanicolaou DA, Unger ER, Vernon SD, Heim C: Chronic atigue syndrome-a clinically empirical approach to its definition and study. BMC Med 2005, 3:19.
5. Sanders P, Korf J: Neuroaetiology of chronic atigue syndrome: an overview. World J Biol Psychiatry 2008, 9:165-171.
6. Lin E, Hwang Y, Wang SC, Gu ZJ, Chen EY: n artificial neural network approach to the drug efficacy of interferon treatments. harmacogenomics 2006, 7:1017-1024.
7. Lin E, Hwang Y, Tzeng CM: A case study of he utility of the HapMap database for pharmacogenomic haplotype analysis in the aiwanese population. Mol Diagn Ther 2006, 10:367-370.
8. Smith AK, White PD, Aslakson E, Vollmer-Conna U, Rajeevan MS: Polymorphisms in genes egulating the HPA axis associated with mpirically delineated classes of unexplained chronic fatigue. Pharmacogenomics 2006, 7:387-394.
9. Goertzel BN, Pennachin C, de Souza Coelho L, urbaxani B, Maloney EM, Jones JF: Combinations of single nucleotide olymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome. Pharmacogenomics 2006, 7:475-483.
10. Rajeevan MS, Smith AK, Dimulescu I, Unger R, Vernon SD, Heim C, Reeves WC: Glucocorticoid receptor polymorphisms and haplotypes associated with chronic fatigue syndrome. Genes Brain Behav 2007, 6:167-176.
11. Smith AK, Dimulescu I, Falkenberg VR, Narasimhan S, Heim C, Vernon SD, Rajeevan MS: Genetic evaluation of the serotonergic system in chronic fatigue syndrome. Psychoneuroendocrinology 2008, 33:188-197.
12. Chung Y, Lee SY, Elston RC, Park T: Odds ratio based multifactor-dimensionality reduction method for detecting gene-gene interactions. Bioinformatics 2007, 23:71-76.
13. Lin E, Hsu SY: A Bayesian approach to gene-gene and gene-environment interactions in chronic fatigue syndrome. Pharmacogenomics 2009, 10:35-42.
14. Lin E, Huang LC: Identification of Significant Genes in Genomics Using Bayesian Variable Selection Methods. Computational Biology and Chemistry: Advances and Applications 2008, 1:13-18.
15. Lee KE, Sha N, Dougherty ER, Vannucci M, Mallick BK: Gene selection: a Bayesian variable selection approach. Bioinformatics 2003, 19:90-97.
16. Lin E, Hwang Y, Liang KH, Chen EY: Pattern-recognition techniques with haplotype analysis in pharmacogenomics. Pharmacogenomics 2007, 8:75-83.
17. Lin E, Hwang Y, Chen EY: Gene-gene and gene-environment interactions in interferon therapy for chronic hepatitis C. Pharmacogenomics 2007, 8:1327-1335.
18. Devi V Dataset from the CDC Chronic Fatigue Syndrome Research Group[http://www.camda.duke.edu/camda06/datasets/index.html]
19. Witten IH, Frank E: Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, CA, USA: Morgan Kaufmann Publishers; 2005.
20. Domingos P, Pazzani M: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 1997, 29:103-137.
21. Vapnik V: The Nature of Statistical Learning Theory. New York, NY, USA: Springer-Verlag; 1995.
22. Burges CJC: A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 1998, 2:127-167.
23. Quinlan JR: C4.5: Programs for Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers; 1993.
24. Breiman L, Friedman JH, Olshen RA, Stone CJ: Classification and regression trees. Boca Raton, FL, USA: CRC Press; 1995.
25. Listgarten J, Damaraju S, Poulin B, Cook L, Dufour J, Driga A, Mackey J, Wishart D, Greiner R, Zanke B: Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms. Clin Cancer Res 2004, 10:2725-2737.
26. Chen K, Kurgan L, Ruan J: Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol 2007, 7:25.
27. Forman G.: An extensive empirical study of feature selection metrics for text classification. J Machine Learning Research 2003, 3:1289-1305.
28. Zheng C, Kurgan L: Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments. BMC Bioinformatics 2008, 9:430.
29. Kohavi R, John GH: Wrappers for feature subset selection. Artificial Intelligence 1997, 97:273-324.
30. Lin E, Hwang Y: A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy. Mol Diagn Ther 2008, 12:219-223.
31. Fawcett T: An introduction to ROC analysis. Pattern Recognit Lett 2006, 27:861-874.
32. Hewett R, Kijsanayothin P: Tumor classification ranking from microarray data. BMC Genomics 2008, 9(Suppl 2):S21.
33. Aliferis CF, Statnikov A, Tsamardinos I, Schildcrout JS, Shepherd BE, Harrell FE Jr: Factors influencing the statistical power of complex data analysis protocols for molecular signature development from microarray data. PLoS One. 2009, 4:e4922.
34. Saeys Y, Inza I, Larrañaga P: A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23:2507-2517.
35. Guyon I, Weston J, Barnhill S, Vapnik V: Gene selection for cancer classification using support vector machines. Machine Learning 2002, 46:389-422.
36. Erdmann G, Berger S, Schütz G: Genetic dissection of glucocorticoid receptorfunction in the mouse brain. J Neuroendocrinol 2008, 20:655-659.
37. Garcia A, Steiner B, Kronenberg G, Bick-Sander A, Kempermann G: Age-dependent expression of glucocorticoid- and mineralocorticoid receptors on neural precursor cell populations in the adult murine hippocampus. Aging Cell 2004, 3:363-371.
38. Whorwood CB, Donovan SJ, Flanagan D, Phillips DI, Byrne CD: Increased glucocorticoid receptor expression in human skeletal muscle cells may contribute to the pathogenesis of the metabolic syndrome. Diabetes 2002, 51:1066-1075.