A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Montazery-Kordy, Hussain; Miranbaygi, Mohammad Hossein; Moradi, Mohammad Hassan

doi:10.1631/jzus.b0820163

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Principle components analysis (PCA) or other multivariate approaches may separate complex differences in more than one continuous variable such as m / z and intensity values between populations of samples in a multidimensional space. ,− In contrast, the much simpler general linear models (GLM) and ANOVA may be used to examine the differences in one continuous variable (univariate) such as intensity mapped to ordinal m / z bins or nominal peptide or protein sequences . Mass spectra of blood peptides between disease states were analyzed by complex multidimensional statistics wherein the m / z and intensity values of the parent ions were considered to be continuous variables analyzed as patterns in a continuous multidimensional space. − A great deal of effort has been invested in an attempt to rigorously analyze the variation in the data with such novel mathematical treatments. − However, MS patterns cannot be applied as a simple statistical test of one protein. ,− In contrast, the reliable analysis of spectra using ANOVA for multiple comparisons was previously accomplished by breaking the m / z scale into 5 m / z windows with analysis of intensity values in the ordinal bins . Here, the use of ANOVA is taken to its logical completion using a relational database of the nominal protein and peptide sequences for organizing log-normal intensity values.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Statistical Analysis of Standard and Human Blood Proteins from Liquid Chromatography, Electrospray Ionization, and Tandem Mass Spectrometry

et al. 2012

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It will be important to determine if the parent and fragment ion intensity results of liquid chromatography, electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) experiments have been randomly and independently sampled from a normal population for the purpose of statistical analysis by general linear models and ANOVA. The tryptic parent peptide and fragment ion m/z and intensity data in the mascot generic files from LC-ESI-MS/MS of purified standard proteins, and human blood protein fractionated by partition chromatography, were parsed into a Structured Query Language (SQL) database and were matched with protein and peptide sequences provided by the X!TANDEM algorithm. The many parent and/or fragment ion intensity values were log transformed, tested for normality, and analyzed using the generic Statistical Analysis System (SAS). Transformation of both parent and fragment intensity values by logarithmic functions yielded intensity distributions that closely approximate the log-normal distribution. ANOVA models of the transformed parent and fragment intensity values showed significant effects of treatments, proteins, and peptides, as well as parent versus fragment ion types, with a low probability of false positive results. Transformed parent and fragment intensity values were compared over all sample treatments, proteins or peptides by the Tukey-Kramer Honestly Significant Difference (HSD) test. The approach provided a complete and quantitative statistical analysis of LC-ESI-MS/MS data from human blood.

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Section: Discussionmentioning

confidence: 99%

Quantitative Statistical Analysis of Standard and Human Blood Proteins from Liquid Chromatography, Electrospray Ionization, and Tandem Mass Spectrometry

et al. 2012

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“…A great deal of effort has been invested in an attempt to rigorously analyze the variation in the data (Villanueva et al, 2004(Villanueva et al, , 2006a(Villanueva et al, ,b, 2007Corr et al, 2006;Mertens et al, 2006;Ragazzi et al, 2006;Rainer et al, 2007;Tucholska et al, 2007;Aresta et al, 2008;McLerran et al, 2008;Schilling & Knapp, 2008;Alexandrov et al, 2009). Novel mathematical treatments of MALDI data have been conceived (Montazery-Kordy et al, 2008;Alexandrov et al, 2009;Liu, 2009). There remains an on-going documentation of blood by MALDI parent ion patterns in response to a variety of physiological states such as disease, drug response, the image or proximity of romantic partners or the use of performance enhancing drugs (Tammen et al, 2002;Guerrier, Lomas, & Boschetti, 2005;Schwegler et al, 2005;de Seny et al, 2005;Orvisky et al, 2006;Ragazzi et al, 2006;Zheng et al, 2006a;Lin et al, 2006b;de Noo et al, 2006b;Au et al, 2007;Cui et al, 2007Cui et al, , 2008bHui et al, 2007;Lim et al, 2007;Taguchi et al, 2007;Tiss et al, 2007;Yildiz et al, 2007;Das et al, 2008;Datta, 2008;Jacot et al, 2008;Kojima et al, 2008;Meuleman et al, 2008Meuleman et al, , 2009…”

Section: Maldi Pattern Analysismentioning

confidence: 99%

Mass spectrometry of peptides and proteins from human blood

Zhu

Bowden

Zhang

et al. 2010

Mass Spectrometry Reviews

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It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

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“…Early attempt at profiling lowmolecular-weight serum proteins has yielded surprisingly good results -sensitivity of 100 %, specificity of 95 % and a positive predictive value of 94 % [18]. Recent studies have continued to deliver similar results, with sensitivity, specificity and positive predictive value all above the 95 % threshold [19,20]. Proteomics also seems to be useful in the classification of information on specific ovarian cancer subtypes, which can be used to tailor pre-surgical therapy [21].…”

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confidence: 97%

Changes in urine autofluorescence in ovarian cancer patients

Birková¹,

A²,

Šteffeková³

et al. 2014

neo

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Ovarian cancer is the type of cancer with the highest mortality rate among gynaecologic malignancies. Due to lack of screening tools, this disease is mainly diagnosed at a progressed stage, when it is too late to adequate therapy. Despite many attempts, enough sensitive and specific biomarker was not still uncovered. Fluorescence spectroscopy has proven to be a useful diagnostic tool with high efficiency. Fluorescence detection has three major advantages over other light-based investigation methods: high sensitivity, high speed, and reliability. Biological materials consist of a number of intrinsic fluorescent compounds -autofluorophores, which are associated with cardinal metabolic pathways. It is well known, that cancerous tissue metabolism is altered compared to healthy one, what influence also intrinsic fluorophores composition of bodily fluids. Urine is one of the biological fluids that could be obtained most easily and displays a blue - green fluorescence that can change in case of pathological process. Analysis of urine autofluorescence is non invasive and simple technique. Using fluorescent spectroscopy, ovarian cancer patients and healthy control group were discerned with high significance, so we predict that fluorescence analysis of urine could be a potential means of ovarian cancer screening.

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A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Abstract: The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.

Cited by 6 publications

References 32 publications

Quantitative Statistical Analysis of Standard and Human Blood Proteins from Liquid Chromatography, Electrospray Ionization, and Tandem Mass Spectrometry

Quantitative Statistical Analysis of Standard and Human Blood Proteins from Liquid Chromatography, Electrospray Ionization, and Tandem Mass Spectrometry

Mass spectrometry of peptides and proteins from human blood

Changes in urine autofluorescence in ovarian cancer patients

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