Comparative analysis of targeted metabolomics: Dominance-based rough set approach versus orthogonal partial least square-discriminant analysis

Journal of Biomedical Informatics

Volumn 53, Issue , 2015, Pages 291-299

  Blasco, H ^a,b,c   Blaszczynski J ^d   Billaut, J C ^b   Nadal Desbarats, L ^a,b   Pradat, P F ^f   Devos, D ^g   Moreau, C ^g   Andres, C R ^a,b,c   Emond, P ^a,b   Corcia, P ^a,b,h   Slowinski R ^d,e  

^a CHRU de Tours   (France)

^b UNIVERSITÉ DE TOURS   (France)

^c UNIVERSITY HOSPITAL OF TOURS   (France)

^d POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

^e SYSTEMS RESEARCH INSTITUTE   (Poland)

^f PITIÉ SALPÊTRIÈRE HOSPITAL   (France)

^g LILLE UNIVERSITY HOSPITAL   (France)

^h CHU BRETONNEAU   (France)

Author keywords

Amyotrophic lateral sclerosis; Bayesian confirmation; Diagnosis prediction; Dominance based rough set approach; Metabolomics; OPLS DA

Indexed keywords

ACETONE; AMINO ACIDS; BIOMARKERS; BIOMOLECULES; DATA HANDLING; DIAGNOSIS; DISCRIMINANT ANALYSIS; METABOLITES; NEURODEGENERATIVE DISEASES; ROUGH SET THEORY;

AMYOTROPHIC LATERAL SCLEROSIS; BAYESIAN CONFIRMATION; DOMINANCE-BASED ROUGH SET APPROACH; METABOLOMICS; OPLS-DA;

MULTIVARIANT ANALYSIS;

3 HYDROXYBUTYRIC ACID; ACETIC ACID; ACETONE; ASCORBIC ACID; GLUTAMINE; PYRUVIC ACID; ACETIC ACID DERIVATIVE; BIOLOGICAL MARKER;

ADULT; AMYOTROPHIC LATERAL SCLEROSIS; ARTICLE; BAYESIAN LEARNING; CLINICAL ARTICLE; COMPARATIVE STUDY; DISCRIMINANT ANALYSIS; DOMINANCE BASED ROUGH SET APPROACH; FEMALE; HUMAN; MALE; METABOLOMICS; ORTHOGONAL PARTIAL LEAST SQUARE DISCRIMINANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; UNIVARIATE ANALYSIS; AGED; ALGORITHM; BAYES THEOREM; BIOLOGY; CHEMISTRY; DECISION MAKING; MIDDLE AGED; MULTIVARIATE ANALYSIS; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PROCEDURES;

3-HYDROXYBUTYRIC ACID; ACETATES; ACETONE; AGED; ALGORITHMS; AMYOTROPHIC LATERAL SCLEROSIS; BAYES THEOREM; BIOMARKERS; COMPUTATIONAL BIOLOGY; DECISION MAKING; DISCRIMINANT ANALYSIS; FEMALE; HUMANS; LEAST-SQUARES ANALYSIS; MAGNETIC RESONANCE SPECTROSCOPY; MALE; METABOLOMICS; MIDDLE AGED; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS;

EID: 84924456509     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2014.12.001     Document Type: Article

References (33)

1. Patti G.J., Yanes O., Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 2012, 13:263-269.
2. Wang J.H., Byun J., Pennathur S. Analytical approaches to metabolomics and applications to systems biology. Semin Nephrol 2010, 30:500-511.
3. Koek M.M., Jellema R.H., van der Greef J., Tas A.C., Hankemeier T. Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives. Metabolomics 2011, 7:307-328.
4. Quinones M.P., Kaddurah-Daouk R. Metabolomics tools for identifying biomarkers for neuropsychiatric diseases. Neurobiol Dis 2009, 35:165-176.
5. Courant F., Royer A.L., Chereau S., Morvan M.L., Monteau F., Antignac J.P., et al. Implementation of a semi-automated strategy for the annotation of metabolomic fingerprints generated by liquid chromatography-high resolution mass spectrometry from biological samples. Analyst 2012, 137:4958-4967.
6. Hoffmann N., Keck M., Neuweger H., Wilhelm M., Hogy P., Niehaus K., et al. Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets. BMC Bioinformatics 2012, 13:214.
7. Eliasson M., Rannar S., Madsen R., Donten M.A., Marsden-Edwards E., Moritz T., et al. Strategy for optimizing LC-MS data processing in metabolomics: a design of experiments approach. Anal Chem 2012, 84:6869-6876.
8. Kirwan G.M., Johansson E., Kleemann R., Verheij E.R., Wheelock A.M., Goto S., et al. Building multivariate systems biology models. Anal Chem 2012, 84:7064-7071.
9. Blasco H., Corcia P., Moreau C., Veau S., Fournier C., Vourc'h P., et al. 1H-NMR-based metabolomic profiling of CSF in early amyotrophic lateral sclerosis. PLoS ONE 2010, 5:e13223.
10. Shaham O., Slate N.G., Goldberger O., Xu Q., Ramanathan A., Souza A.L., et al. A plasma signature of human mitochondrial disease revealed through metabolic profiling of spent media from cultured muscle cells. Proc Natl Acad Sci USA 2010, 107:1571-1575.
11. van den Berg R.A., Hoefsloot H.C., Westerhuis J.A., Smilde A.K., van der Werf M.J. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom 2006, 7:142.
12. Kemsley E.K., Le Gall G., Dainty J.R., Watson A.D., Harvey L.J., Tapp H.S., et al. Multivariate techniques and their application in nutrition: a metabolomics case study. Br J Nutr 2007, 98:1-14.
13. Madsen R., Lundstedt T., Trygg J. Chemometrics in metabolomics - a review in human disease diagnosis. Anal Chim Acta 2010, 659:23-33.
14. Cloarec O., Dumas M.E., Trygg J., Craig A., Barton R.H., Lindon J.C., et al. Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabolomic studies. Anal Chem 2005, 77:517-526.
15. Henderson A.R. The bootstrap: a technique for data-driven statistics. Using computer-intensive analyses to explore experimental data. Clin Chim Acta 2005, 359:1-26.
16. Westerhuis J.A., van Velzen E.J., Hoefsloot H.C., Smilde A.K. Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics 2010, 6:119-128.
17. Pawlak Z. Rough sets. Theoretical aspects of reasoning about data 1991, Kluwer, Dordrecht.
18. Błaszczyński J., Greco S., Słowiński R., Szelag M. Monotonic variable consistency rough set approaches. Int J Approximate Reasoning 2009, 50:979-999.
19. Greco S., Matarazzo B., Słowiński R. Rough sets theory for multicriteria decision analysis. Eur J Oper Res 2001, 129:1-47.
20. Slowinski R., Greco S., Matarazzo B. Rough sets in decision making. Encyclopedia of complexity and systems science 2009, 7753-7786. Springer, New York. R.A. Meyers (Ed.).
21. Słowiński R., Greco S., Matarazzo B. Rough-set-based decision support 2014, 557-609. Springer, New York. Search methodologies: introductory tutorials in optimization and decision support techniques, 2nd ed. E. Kendall (Ed.).
22. Greco S., Matarazzo B., Słowiński R. Multicriteria classification. Handbook of data mining and knowledge discovery 2002, 318-328. Oxford University Press, New York. J. Zytkow WKa (Ed.).
23. Greco S., Matarazzo B., Slowinski R. Rough sets methodology for sorting problems in presence of multiple attributes and criteria. Eur J Oper Res 2002, 138:247-259.
24. Błaszczyński J., Greco S., Słowiński R. Inductive discovery of laws using monotonic rules. Eng Appl Artif Intell 2012, 25:284-294.
25. Błaszczyński J., Słowiński R., Szelag M. Sequential covering rule induction algorithm for variable consistency rough set approaches. Inf Sci 2011, 181:987-1002.
26. Błaszczyński J, Słowiński R, Stefanowski. J Proc of the workshop from local to global models: feature set-based consistency sampling in bagging ensembles. In: Knobe A, JF, editor. European Conference on Machine Learning & Principles of Knowledge Discovery in Databases (ECML/PKDD 2009). Bled, Slovenia; 2009. p. 19-35.
27. Błaszczyński J., Słowiński R., Stefanowski J. Variable consistency bagging ensembles. Transactions on rough sets XI (LNCS 5946) 2010, Springer, Berlin, p. 40-52.
28. Błaszczyński J., Słowiński R., Susmaga R. Rule-based estimation of attribute relevance. RSKT 2011, LNCS 6954 2011, 36-44. Springer, Berlin. J. al (Ed.).
29. Pradat P.F., Bruneteau G., Gordon P.H., Dupuis L., Bonnefont-Rousselot D., Simon D., et al. Impaired glucose tolerance in patients with amyotrophic lateral sclerosis. Amyotroph Lateral Scler 2009, 1-6.
30. Blasco H., Corcia P., Pradat P.F., Bocca C., Gordon P.H., Veyrat-Durebex C., et al. Metabolomics in cerebrospinal fluid of patients with amyotrophic lateral sclerosis: an untargeted approach via high-resolution mass spectrometry. J Proteome Res 2013.
31. Kumar A., Bala L., Kalita J., Misra U.K., Singh R.L., Khetrapal C.L., et al. Metabolomic analysis of serum by (1) H NMR spectroscopy in amyotrophic lateral sclerosis. Clin Chim Acta 2010.
32. Parakh S., Spencer D.M., Halloran M.A., Soo K.Y., Atkin J.D. Redox regulation in amyotrophic lateral sclerosis. Oxid Med Cell Longev 2013, 2013:408681.
33. Wallis N., Zagami C.J., Beart P.M., O'Shea R.D. Combined excitotoxic-oxidative stress and the concept of non-cell autonomous pathology of ALS: insights into motoneuron axonopathy and astrogliosis. Neurochem Int 2012.