Construction and verification of mathematical model of mass spectrometry data

• Autorzy: Plechawska-Wójcik M.

Warianty tytułu

PL

Konstrukcja i weryfikacja matematycznego modelu danych widm masowych

• Języki publikacji: EN

Abstrakty

EN

The article presents issues concerning construction, adjustment and implementation of mass spectrometry mathematical model based on Gaussians and Mixture Models and the mean spectrum. This task is essential to the analysis and it needs specification of many parameters of the model.

PL

Artykuł przedstawia kwestie związane z konstrukcją, dopasowaniem i implementacją modelu matematycznego widm masowych opartego o rozkłady normalne i mieszaniny rozkładów oraz o widmo średnie. To zadanie jest kluczowe dla analizy, wymaga też określenia wielu parametrów modelu.

Słowa kluczowe

EN

Maldi-Tof mass spectrometry; Gaussians; Gaussian mixture models; SVM-RFE classification

PL

spektrometria masowa Maldi-Tof; rozkłady Gaussa; mieszaniny rozkładów Gaussa; klasyfikacja SVM-RFE

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2013
• Tom: nr 1
• Strony: 9--14
• Opis fizyczny: Bibliogr. 39 poz., rys.

Twórcy

autor

Plechawska-Wójcik M.

• Politechnika Lubelska, Wydział Elektrotechniki i Informatyki, Instytut Informatyki

Bibliografia

• [1] Akaike H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control, 9 s.716–723, 1974.
• [2] Baggerly K.A., Morris J., Wang J., Gold D., Xiao L.C., Coombes K.R.: A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization time of flight proteomics spectra from serum samples. Proteomics, s. 1667–1672, 2003.
• [3] Banfield J., Raftery A.: Model-based Gaussian and non-Gaussian clustering. Biometrics, 49 s. 803–821, 1993.
• [4] Boster B., Guyon I., Vapnik V.: A training algorithm for optimal margin classifiers. Fifth Annual Workshop on Computational Learning Theory, s. 114– 152, 1992.
• [5] Bozdogan H.: Choosing the number of component clusters in the mixturemodel using a new informational complexity criterion of the inverse-fisher informational matrix. Springer-Verlag,Heidelberg, 19 s. 40–54, 1993.
• [6] Bozdogan H.: On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models. Communications in Statictics, Theory and Methods, 19 s. 221–278, 1990.
• [7] Celeux G., Soromenho G.: An entropy criterion for assessing the number of clusters in a mixture model. Classification Journal, 13, s. 195–212, 1996.
• [8] Clyde M.A., House L.L., Wolpert R.L.: Nonparametric models for proteomic peak identification and quatification. ISDS Discussion Paper, s. 2006–2007, 2006.
• [9] Coombes K., Baggerly K., Morris J.: Pre-processing mass spectrometry data, Fundamentals of Data Mining in Genomics and Proteomics, W D ubitzky, M Granzow, and D Berrar, eds. Kluwer, s. 79-99. 2007, Boston.
• [10] Coombes K.R., Koomen J.M., Baggerly K.A., Morris J., Kobayashi R.: Understanding the characteristics of mass spectrometry data through the use of simulation. Cancer Informatics, 1 s. 41–52, 2005.
• [11] Comon P.: Independent component analysis – a new concept? Signal Processing, 36 s. 287–314, 1994.
• [12] Dempster A.P., Laird N.M., Rubin D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc., 39,1 s. 1-38, 1977.
• [13] Du P., Kibbe W., Lin S.: Improved peak detection in mass spectrum by incorporating continous wavelet transform-based pattern matching. Genome analysis, 22 s. 2059-2065, 2006.
• [14] Dubitzky W., Granzow M., Berrar D.: Fundamentals of data mining in genomics and proteomics. Springer, Kluwer Boston, 2007.
• [15] Fung E.T., Enderwick C.: Proteinchip clinical proteomics: computational challenges and solutions. Biotechniques, Suppl., 32 s. 34–41, 2002.
• [16] Gyaourova A., Kamath C., Fodor I.K.: Undecimated wavelet transforms for image de-noising. Technical Report UCRL-ID-150931, Lawrence Livermore National Laboratory, Livermore, CA, 2002.
• [17] Gentzel M., Kocher T., Ponnusamy S., Wilm M.: Preprocessing of tandem mass spectrometric data to support automatic protein identyfication. Proteomics, 3, s. 1597–1610, 2003.
• [18] Gras R., Muller M., Gasteiger E., Gay S., Binz P.A., Bienvenut W., Hoogland C., Sanchez J.C., Bairoch A., Hochstrasser D.F., Appel R.D.: Improving protein identification from peptide mass fingerprinting through a parameterized multi-level scoring algorithm and an optimized peak detection. Electrophoresis, 20 s. 3535-3550, 1999.
• [19] Jutten C., H´erault J.. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24 s. 1-10, 1991.
• [20] Lang M., Guo H., Odegard J.E., Burrus C.S., Well R.O.Jr.: Nonlinear processing of a shift invariant DWT for noise reduction. Proc. SPIE. Wavelet Applications II, 2491 s. 640-651, 1995.
• [21] Lang M., Guo H., Odegard J.E., Burrus C.S., Well R.O.Jr.: Noise reduction using an undecimated discrete wavelet transform. IEEE Signal Processing Letters, 3 s. 10-12, 1996.
• [22] Lewandowicz A., Bakun M., Imiela J., Dadlez M.: Proteomika w uronefrologii - nowe perspektywy diagnostyki nieinwazyjnej? Nefrologia i dializoterapia polska, 1 s. 15–21, 2009.
• [23] Mantini D., Petrucci F., Pieragostino D., Del Boccio P., Di Nicola M., Di Ilio C., Federici G., Sacchetta P., Comani S., Urbani A.: Limpic: a computational method for the separation of protein signals from noise. BMC Bioinformatics, 8:101, 2007.
• [24] Mantini D., Petrucci F., Del Boccio P., Pieragostino D., Di Nicola M., Lugaresi A., Federici G., Sacchetta P., Di Ilio C., Urbani A.: Independent component analysis for the extraction of reliable protein signal profiles from Maldi-ToF mass spectra. Bioinformatics, 24 s.63 – 70, 2008.
• [25] McLachlan G.: Finite mixture models. John Wiley and Sons, 2001.
• [26] Morris J., Coombes K., Kooman J., Baggerly K., Kobayashi R..: Feature extraction and quantification for mass spectrometry data in biomedical applications using the mean spectrum. Bioinformatics, 21(9): 1764-1775. 2005.
• [27] Norris J., Cornett D., Mobley J., Anderson M., Seeley E., Chaurand P, Caprioli R.: Processing MALDI mass spectra to improve mass spectral direct tissue analysis. National institutes of health. 2007, USA.
• [28] Plechawska-Wójcik M.: Comprehensive analysis of mass spectrometry data – a case study. Foundations of Computing and Decision Sciences. Vol. 36 - No. 3-4, s. 275-292, 2011.
• [29] Plechawska M.: Comparing and similarity determining of gaussian distributions mixtures. Polish Journal of Environmental Studies, 17, No. 3B s. 341–346, 2008.
• [30] Polanska J., Plechawska M.: Comparison of convergence criterions used in expectation-maximization algorithm. Symbiosis, 2008.
• [31] Randolph T., Mithcell B., McLerran D., Lampe P., Feng Z.: Quantifying peptide signal in maldi-tof mass spectrometry data. Molecular & Cellular Proteomics, 4 s. 1990–1999, 2005.
• [32] Schwarz G.: Estimating the dimension of a model. Annals of Statistics, 6 s . 461–464, 1978.
• [33] Tibshirani R., Hastiey T., Narasimhanz B., Soltys S., Shi G., Koong A., Le Q.T.: Sample classification from protein mass spectrometry, by ’peak probability contrasts’. Bioinformatics, 20 s. 3034 – 3044, 2004.
• [34] Tversky A., Hutchinson J.W.: Nearest neighbor analysis of psychological spaces. Psychological review, 93(1) s. 3–22, 1993.
• [35] Vapnik V.N.: The Nature of Statistical Learning Theory. Springer, 1995.
• [36] Vapnik V.N.: Statistical Learning Theory. Wiley, 1998.
• [37] Windham M.P. Cutler A.: Information ratios for validating cluster analyses. Journal of the American Statistical Association, 87 s. 1188–1192, 1993.
• [38] Wold H.: Estimation of principal components and related models by iterative least squares. Multivariate Analysis, s. 391–420, 1966.
• [39] Yasui Y., Pepe M., Thompson M.L., Adam B.L., Wright G.L., Qu Y., Potter J.D., Winget M., Thornquist M., Feng Z.: A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics, 4 s. 449-463, 2003.