Classification of Proteomic MS Data as Bayesian Solution of an Inverse Problem

Szacherski, Pascal; Giovannelli, Jean‐François; Gerfault, Laurent; Mahé, Pierre; Charrier, Jean-Philippe; Giremus, Audrey; Lacroix, Bruno; Grangeat, Pierre

doi:10.1109/access.2014.2359979

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…Another classifier for mass spectrometry data has been proposed by Pascal et.al in [49] for early detection. They used inversion classification which includes an inversion problem with a combined Bayesian method.…”

Section: Discussionmentioning

confidence: 99%

Big data Analysis in Plant Science and Machine Learning Tool Applications in Genomics and Proteomics

Khiarak

Valizadeh-Kamran

Heydariyan

et al. 2018

International Journal of Computational and Experimental Science and Engineering

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Data extensions in plant biology and drastically increasing data volume in this field impose the scientists analyzing data by means of smart computer systems. Since, manually analyzing huge amount of data is cumbersome and even impossible. A comparative study of proteins a wide scale, is the proteomics knowledge. Nowadays, the proteomics analysis is considered as one of the most important methods in genomics and of the gene expression studies. Large amounts of data are big challenges in plant biology. Biological communities either need to create data making compatible with the parallel computing and the data management associated with its infrastructures or are looking for novel analytical patterns to extract information from a large amount of data. Machine learning provides promising analytical and computational solutions for large, heterogeneous, non-structured datasets for large-scale data, especially for the proteomics data. In particular, a conceptual review and applicable methods of machine learning are described by predicting that how machine learning with massive data technology can be an interface to facilitate basic researches and biotechnology plant sciences.

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Section: Discussionmentioning

confidence: 99%

Big data Analysis in Plant Science and Machine Learning Tool Applications in Genomics and Proteomics

Khiarak

Valizadeh-Kamran

Heydariyan

et al. 2018

International Journal of Computational and Experimental Science and Engineering

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“…The BHI algorithm has to solve the inverse problem and compute protein concentration y and technical parameters θ tech . This problem is solved in a Bayesian framework [ 8 – 15 ]. Table 3 shows the distribution type used for each variable in this Bayesian framework.…”

Section: Methodsmentioning

confidence: 99%

Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry

Klich¹,

Mercier²,

Gerfault³

et al. 2018

BMC Bioinformatics

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BackgroundIn the field of biomarker validation with mass spectrometry, controlling the technical variability is a critical issue. In selected reaction monitoring (SRM) measurements, this issue provides the opportunity of using variance component analysis to distinguish various sources of variability. However, in case of unbalanced data (unequal number of observations in all factor combinations), the classical methods cannot correctly estimate the various sources of variability, particularly in presence of interaction. The present paper proposes an extension of the variance component analysis to estimate the various components of the variance, including an interaction component in case of unbalanced data.ResultsWe applied an experimental design that uses a serial dilution to generate known relative protein concentrations and estimated these concentrations by two processing algorithms, a classical and a more recent one. The extended method allowed estimating the variances explained by the dilution and the technical process by each algorithm in an experiment with 9 proteins: L-FABP, 14.3.3 sigma, Calgi, Def.A6, Villin, Calmo, I-FABP, Peroxi-5, and S100A14. Whereas, the recent algorithm gave a higher dilution variance and a lower technical variance than the classical one in two proteins with three peptides (L-FABP and Villin), there were no significant difference between the two algorithms on all proteins.ConclusionsThe extension of the variance component analysis was able to estimate correctly the variance components of protein concentration measurement in case of unbalanced design.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2075-8) contains supplementary material, which is available to authorized users.

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“…The classical signal‐processing algorithm was developed by a proteomic platform (CLIPP, Dijon, France). The new signal processing, “BHI‐PRO” was developed as part of BHI‐PRO project dedicated to Bayesian hierarchical inversion for mass spectrometry and its application to discovery and validation of new protein biomarkers (Dridi et al., ; Gerfault et al., ; Gerfault et al., ; Grangeat et al., ; Szarcherski, ; Szarcherski et al., ; Szacherski et al., ).…”

Section: Methodsmentioning

confidence: 99%

Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI‐TOF mass spectrometry

et al. 2017

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Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algorithm and two more recent ones (BHI-PRO 1 and 2). Given the high technological variability, the quantification failed to restitute the known quantities of five out of nine proteins present in a controlled solution. There was a linear relationship between protein quantities and peak intensities for four out of nine peaks with all algorithms. The biological component of the variance was higher with BHI-PRO than with the classical algorithm (80-95% with BHI-PRO 1, 79-95% with BHI-PRO 2 vs. 56-90%); thus, BHI-PRO were more efficient in protein quantification. The technological component of the variance was higher with the classical algorithm than with BHI-PRO (6-25% vs. 2.5-9.6% with BHI-PRO 1 and 3.5-11.9% with BHI-PRO 2). The chemical component was also higher with the classical algorithm (3.6-18.7% vs. < 3.5%). Thus, BHI-PRO were better in removing noise from signal when the expected peaks are detected. Overall, either BHI-PRO algorithm may reduce the technological variance from 25 to 10% and thus improve protein quantification and biomarker validation.

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Classification of Proteomic MS Data as Bayesian Solution of an Inverse Problem

Cited by 11 publications

References 40 publications

Big data Analysis in Plant Science and Machine Learning Tool Applications in Genomics and Proteomics

Big data Analysis in Plant Science and Machine Learning Tool Applications in Genomics and Proteomics

Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry

Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI‐TOF mass spectrometry

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