Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform (original) (raw)
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Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation
Bioinformatics, 2009
Automatic classification of high-resolution mass spectrometry proteomic data has increasing potential in the early diagnosis of cancer. We propose a new procedure of biomarker discovery in serum protein profiles based on: (i) discrete wavelet transformation of the spectra; (ii) selection of discriminative wavelet coefficients by a statistical test and (iii) building and evaluating a support vector machine classifier by double cross-validation with attention to the generalizability of the results. In addition to the evaluation results (total recognition rate, sensitivity and specificity), the procedure provides the biomarker patterns, i.e. the parts of spectra which discriminate cancer and control individuals. The evaluation was performed on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) serum protein profiles of 66 colorectal cancer patients and 50 controls. Results: Our procedure provided a high recognition rate (97.3%), sensitivity (98.4%) and specificity (95.8%). The extracted biomarker patterns mostly represent the peaks expressing mean differences between the cancer and control spectra. However, we showed that the discriminative power of a peak is not simply expressed by its mean height and cannot be derived by comparison of the mean spectra. The obtained classifiers have high generalization power as measured by the number of support vectors. This prevents overfitting and contributes to the reproducibility of the results, which is required to find biomarkers differentiating cancer patients from healthy individuals. Availability: The data and scripts used in this study are available at
2008
In the present contribution we propose two recently developed classification algorithms for the analysis of massspectrometric dataçthe supervised neural gas and the fuzzy-labeled self-organizing map. The algorithms are inherently regularizing, which is recommended, for these spectral data because of its high dimensionality and the sparseness for specific problems. The algorithms are both prototype-based such that the principle of characteristic representants is realized. This leads to an easy interpretation of the generated classifcation model. Further, the fuzzy-labeled self-organizing map is able to process uncertainty in data, and classification results can be obtained as fuzzy decisions. Moreover, this fuzzy classification together with the property of topographic mapping offers the possibility of class similarity detection, which can be used for class visualization. We demonstrate the power of both methods for two exemplary examples: the classification of bacteria (listeria types) and neoplastic and non-neoplastic cell populations in breast cancer tissue sections.
Computer Methods and Programs in Biomedicine, 2010
In this study, a pattern recognition system is presented for improving the classification accuracy of MS-spectra by means of gathering information from different MS-spectra intensity regions using a majority vote ensemble combination. The method starts by automatically breaking down all MS-spectra into common intensity regions. Subsequently, the most informative features (m/z values), which might constitute potential significant biomarkers, are extracted from each common intensity region over all the MS-spectra and, finally, normal from ovarian cancer MS-spectra are discriminated using a multi-classifier scheme, with members the Support Vector Machine, the Probabilistic Neural Network and the k-Nearest Neighbour classifiers. Clinical material was obtained from the publicly available ovarian proteomic dataset (8-7-02). To ensure robust and reliable estimates, the proposed pattern recognition system was evaluated using an external cross-validation process. The average overall performance of the system in discriminating normal from cancer ovarian MS-spectra was 97.18% with 98.52% mean sensitivity and 94.84% mean specificity values.
Lecture Notes in Computer Science, 2006
Mass spectrometry is becoming an important tool in biological sciences. Tissue samples or easily obtained biological fluids (serum, plasma, urine) are analysed by a variety of mass spectrometry methods, producing spectra characterized by very high dimensionality and a high level of noise. Here we address a feature exraction method for mass spectra which consists of two main steps : In the first step an algorithm for low level preprocessing of mass spectra is applied, including denoising with the Shift-Invariant Discrete Wavelet Transform (SIDWT), smoothing, baseline correction, peak detection and normalization of the resulting peak-lists. After this step, we claim to have reduced dimensionality and redundancy of the initial mass spectra representation while keeping all the meaningful features (potential biomarkers) required for disease related proteomic patterns to be identified. In the second step, the peak-lists are alligned and fed to a Support Vector Machine (SVM) which classifies the mass spectra. This procedure was applied to SELDI-QqTOF spectral data collected from normal and ovarian cancer serum samples. The classification performance was assessed for distinct values of the parameters involved in the feature extraction pipeline. The method described here for low-level preprocessing of mass spectra results in 98.3% sensitivity, 98.3% specificity and an AUC (Area Under Curve) of 0.981 in spectra classification.
PLoS ONE, 2011
The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called ''omics'' disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n%p constraint, and as such, require pretreatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.
Robust SVM-Based Biomarker Selection with Noisy Mass Spectrometric Proteomic Data
Lecture Notes in Computer Science, 2006
Computational analysis of mass spectrometric (MS) proteomic data from sera is of potential relevance for diagnosis, prognosis, choice of therapy, and study of disease activity. To this aim, feature selection techniques based on machine learning can be applied for detecting potential biomarkes and biomaker patterns. A key issue concerns the interpretability and robustness of the output results given by such techniques. In this paper we propose a robust method for feature selection with MS proteomic data. The method consists of the sequentail application of a filter feature selection algorithm, RELIEF, followed by multiple runs of a wrapper feature selection technique based on support vector machines (SVM), where each run is obtained by changing the class label of one support vector. Frequencies of features selected over the runs are used to identify features which are robust with respect to perturbations of the data. This method is tested on a dataset produced by a specific MS technique, called MALDI-TOF MS. Two classes have been artificially generated by spiking. Moreover, the samples have been collected at different storage durations. Leave-one-out cross validation (LOOCV) applied to the resulting dataset, indicates that the proposed feature selection method is capable of identifying highly discriminatory proteomic patterns.
Wavelet-based procedures for proteomic mass spectrometry data processing
Computational Statistics & Data Analysis, 2007
Proteomics aims at determining the structure, function and expression of proteins. High-throughput mass spectrometry (MS) is emerging as a leading technique in the proteomics revolution. Though it can be used to find disease-related protein patterns in mixtures of proteins derived from easily obtained samples, key challenges remain in the processing of proteomic MS data. Multiscale mathematical tools such as wavelets play an important role in signal processing and statistical data analysis.A wavelet-based algorithm for proteomic data processing is developed. A MATLAB implementation of the software package, called WaveSpect0, is presented including processing procedures of step-interval unification, adaptive stationary discrete wavelet denoising, baseline correction using splines, normalization, peak detection, and a newly designed peak alignment method using clustering techniques. Applications to real MS data sets for different cancer research projects in Vanderbilt Ingram Cancer Center show that the algorithm is efficient and satisfactory in MS data mining.
2006
Pathological changes within an organ might be reflected in proteomic patterns in serum. Mass spectrometry is becoming an important tool that generates the proteomic Patterns. Mass spectrometry yields complex functional data for which the features of scientific interest are the peaks. Due to this complexity of data, a higher order analysis such as wavelet transform is needed to uncover the differences in proteomic patterns. We have applied wavelet based feature extraction method to available data and used a filter approach to feature subset selection in order to identify the appropriate biomarkers from reconstructed mass spectra. Using different classification algorithms, our approach yielded an accuracy of 98%, specificity of 97%, and sensitivity of 100%.
Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis
Cancer Informatics, 2007
A key challenge in clinical proteomics of cancer is the identifi cation of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specifi c to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The fi rst two steps are the selection of the most discriminating biomarkers with a construction of different classifi ers. Finally, we compare and validate their performance and robustness using different supervised classifi cation methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classifi cation Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.
Mass Spectrometry-Based Proteomic Data for Cancer Diagnosis using Interval Type-2 Fuzzy System
2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015
An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.