Application of wavelet transforms and an approximate deconvolution method for the resolution of noisy overlapped peaks in DNA capillary electrophoresis

Olazábal, Virginia; Prasad, Lakshman; Stark, Peter; Olivares, José A.

doi:10.1039/b312516p

Cited by 15 publications

(8 citation statements)

References 57 publications

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“…In addition, strategies to process data obtained from high-performance liquid chromatography (HPLC) and capillary electrophoresis (CE) [61,62], or a multi-batch approach, combined with multiple-wavelength chromatograms for 1D data analysis, may be extended to GC deconvolution [63].…”

Section: Gc×gc Data Deconvolutionmentioning

confidence: 99%

Interpretation of comprehensive two-dimensional gas chromatography data using advanced chemometrics

Zeng

Hügel

et al. 2014

TrAC Trends in Analytical Chemistry

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HIGHLIGHTS• We present a comprehensive survey of GC×GC and GC×GC-MS data with chemometrics • This review includes principles, theories and graphical tools for data processing • We discuss deconvolution of 1D, coupled and GC×GC separations with FID/MS • We consider retention, structure, t R shifts, orthogonality and image analysis ABSTRACT The power of comprehensive two-dimensional gas chromatography (GC×GC) for the study of complex mixtures has been indisputably proved in the past several decades. This review encompasses the whole of GC×GC-related data processing and summarizes relevant applications. We include theoretical introduction to some specific methods and studies to aid readers' understanding of chemometrics strategies for advanced data interpretation.

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Section: Gc×gc Data Deconvolutionmentioning

confidence: 99%

Interpretation of comprehensive two-dimensional gas chromatography data using advanced chemometrics

Zeng

Hügel

et al. 2014

TrAC Trends in Analytical Chemistry

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“…Wavelet transform (WT) which biological signals are represented in both spatial and frequency domains [7] has been effectively used in the feature extraction stage of classification [8][9][10][11][12][13][14][15][16][17][18][19][20]. More specifically, WT has been widely applied for denoising DNA CE signals that have overlapped peaks [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy

YİĞİT¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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DNA sequence classification is an important challenge in genomic studies due to non-linear and chaotic behavior of DNA oxidation signals of Adenine, Cytosine, Guanine, and Thymine bases. To achieve genotype identification of samples derived from biological sources accurately, Machine Learning (ML) methods have been commonly preferred instead of expert-based methods due to the ability in handling such these complex-structured biological sequences. Reducing the dimension without sacrificing important information that should not be omitted during the classification process is an important task in ML applications. This st udy presents a new feature extraction method to detect two sub-types of hepatitis nucleic acid trace files. The proposed method combines both discrete wavelet transform (DWT) and entropy. The DWT decomposes the bases signals up to three levels and thus all necessary information that is hidden in both spatial and frequency domains is aimed to captured. To achieve a good summarization of DNA trace files having different length, multi-scale permutation entropy (MPE) measures are then computed from approximate and detail coefficients o f signals s tored in the s ub-bands. Different feature sets are extracted with the proposed method using real data covering 200 hepatitis DNA trace files and then fed to a simple memory-based learning classifier, k-NN. The classification performance of the proposed feature extraction method is compared against a method based on MPE features without wavelet decomposition. The results indicate, in classifying hepatitis DNA trace files, the average accuracy reaches up to nearly 99% with feature sets based on proposed method even at 30% training samples proportion. Cite this article as: Yiğit ÖE, Öz E. Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy. Sigma

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“…Nonetheless, the quantification difficulties from poorly resolved peaks may be overcome mathematically by using chemometric techniques. Multivariate curve resolution methods [10][11][12], augmented iterative target transformation factor analysis [1], wavelet transforms [13] and second-order derivative electropherograms [14,15] are, among other approaches, the most recently reported for this purpose using the second-order data from CZE-DAD. In the last decade, artificial neural networks (ANNs) have shown their unique merits regarding the great variety of chemometric approaches reported for the classification and regression purposes in many fields of analytical chemistry.…”

Section: Introductionmentioning

confidence: 99%

Combining classification and regression approaches for the quantification of highly overlapping capillary electrophoresis peaks by using evolutionary sigmoidal and product unit neural networks

Hervás¹,

Gutiérrez²,

Silva³

et al. 2007

Journal of Chemometrics

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This is a study of the potential of neural networks built by using different transfer functions (sigmoidal, product and sigmoidal-product units) designed by an evolutionary algorithm to quantify highly overlapping electrophoretic peaks. To test this approach, two aminoglycoside antibiotics, amikacin and paramomycin, were quantified from samples containing either only one component or mixtures of them though capillary zone electrophoresis (CZE) with laser-induced fluorescence (LIF) detection. The three models assayed used as input data the four-parameter Weibull curve associated with the profile of the electrophoretic peak and in some cases the class label for each sample estimated by cluster analysis. The combination of classification and regression approaches allowed the establishment of straightforward network topologies enabling the analytes to be quantified with great accuracy and precision. The best models for mixture samples were provided by product unit neural networks (PUNNs), 4:4:1 (14 weights) for both analytes, after discrimination by cluster analysis, allowing the analytes to be quantified with great accuracy: 8.2% for amikacin and 5.6% for paromomycin within the standard error of prediction for the generalization test, SEP G . For comparison, partial least square regression was also used for the resolution of these mixtures; it provided a minor accuracy: SEP G 11.8 and 15.7% for amikacin and paramomycin, respectively. The reduced dimensions of the neural networks models selected enabled the derivation of simple quantification equations to transform the input variables into the output variable. These equations can be more easily interpreted from a chemical point of view than those provided by other ANN models.

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Application of wavelet transforms and an approximate deconvolution method for the resolution of noisy overlapped peaks in DNA capillary electrophoresis

Cited by 15 publications

References 57 publications

Interpretation of comprehensive two-dimensional gas chromatography data using advanced chemometrics

Interpretation of comprehensive two-dimensional gas chromatography data using advanced chemometrics

Feature extraction for DNA capillary electrophesis signals based on discrete wavelet transform combined with multi-scale permutation entropy

Combining classification and regression approaches for the quantification of highly overlapping capillary electrophoresis peaks by using evolutionary sigmoidal and product unit neural networks

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