Statistical discrimination of steroid profiles in doping control with support vector machines

Due to their performance enhancing properties, use of anabolic steroids (e.g. testosterone, nandrolone, etc.) is banned in elite sports. Therefore, doping control laboratories accredited by the World Anti-Doping Agency (WADA) screen among others for these prohibited substances in urine. It is particularly challenging to detect misuse with naturally occurring anabolic steroids such as testosterone (T), which is a popular ergogenic agent in sports and society.     

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

Simultaneous voltammetric determination of morphine and noscapine by adsorptive differential pulse stripping method and least-squares support vector machines

An adsorptive differential pulse stripping method for the simultaneous determination of morphine and noscapine is proposed. The procedure involves an adsorptive accumulation of morphine and noscapine on a hanging mercury drop electrode (HMDE), followed by oxidation of adsorbed morphine and noscapine by voltammetric scan using differential pulse modulation. The optimum experimental conditions are: pH 10.0, accumulation potential of −100 mV versus Ag/AgCl, accumulation time of 150 s, scan rate of 40 mV s⁻¹ and pulse height of 100 mV. Morphine and noscapine peak currents were observed in same potential region at about +0.25 V. The simultaneous determination of morphine and noscapine by using voltammetry is a difficult problem in analytical chemistry, due to voltammogram interferences. The resolution of mixture of morphine and noscapine by the application of least-squares support vector machines (LS-SVM) was performed. The linear dynamic ranges were 0.01-3.10 and 0.015-2.75 μg mL⁻¹ and detection limits were 3 and 7 ng mL⁻¹ for morphine and noscapine, respectively. The capability of the method for the analysis of real samples was evaluated by the determination of morphine and noscapine in addict's human plasma with satisfactory results.     

Prediction of protein secondary structure content using support vector machine

In this paper, the support vector machine was trained to grasp the relationship between the pair-coupled amino acid composition and the content of protein secondary structural elements, including -helix, 3₁₀-helix, π-helix, β-strand, β-bridge, turn, bend and the rest random coil. Self-consistency and cross validation tests were made to assess the performance of our method. Results superior to or competitive with the popular theoretical and experimental methods have been obtained.     

Using support vector machines for prediction of protein structural classes based on discrete wavelet transform

The prediction of secondary structure is a fundamental and important component in the analytical study of protein structure and functions. How to improve the predictive accuracy of protein structural classification by effectively incorporating the sequence‐order effects is an important and challenging problem. In this study, a new method, in which the support vector machine combines with discrete wavelet transform, is developed to predict the protein structural classes. Its performance is assessed by cross‐validation tests. The predicted results show that the proposed approach can remarkably improve the success rates, and might become a useful tool for predicting the other attributes of proteins as well. © 2008 Wiley Periodicals, Inc. J Comput Chem 2009     

Development of a sugar-binding residue prediction system from protein sequences using support vector machine

Several methods have been proposed for protein–sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (https://doi.org/10.5281/zenodo.61513).     

Application of support vector machines to 1H NMR data of fish oils: methodology for the confirmation of wild and farmed salmon and their origins

Support vector machines (SVMs) were used as a novel learning machine in the authentication of the origin of salmon. SVMs have the advantage of relying on a well-developed theory and have already proved to be successful in a number of practical applications. This paper provides a new and effective method for the discrimination between wild and farm salmon and eliminates the possibility of fraud through misrepresentation of the country of origin of salmon. The method requires a very simple sample preparation of the fish oils extracted from the white muscle of salmon samples. (1)H NMR spectroscopic analysis provides data that is very informative for analysing the fatty acid constituents of the fish oils. The SVM has been able to distinguish correctly between the wild and farmed salmon; however ca. 5% of the country of origins were misclassified.     

Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties

T-lymphocyte (T-cell) is a very important component in human immune system. It possesses a receptor (TCR) that is specific for the foreign epitopes which are in a form of short peptides bound to the major histocompatibility complex (MHC). When T-cell receives the message about the peptides bound to MHC, it makes the immune system active and results in the disposal of the immunogen. The antigenic determinants recognized and bound by the T-cell receptor is known as T-cell epitope. The accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. For the first time we developed new models using least squares support vector machine (LSSVM) and amino acid properties for T-cell epitopes prediction. A dataset including 203 short peptides (167 non-epitopes and 36 epitopes) was used as the input dataset and it was randomly divided into a training set and a test set. The models based on LSSVM and amino acid properties were evaluated using leave-one-out cross-validation method and the predictive ability of the test set, and obtained the results of 0.9875 and 0.9734 under the ROC curves, respectively. This result is more satisfactory than that were reported before. Especially, the accuracy of true positive gets a marked enhancement.     

Variable selection using probability density function similarity for support vector machine classification of high-dimensional microarray data

One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. To circumvent the problem, a new gene mining approach is proposed based on the similarity between probability density functions on each gene for the class of interest with respect to the others. This method allows the ascertainment of significant genes that are informative for discriminating each individual class rather than maximizing the separability of all classes. Then one can select genes containing important information about the particular subtypes of diseases. Based on the mined significant genes for individual classes, a support vector machine with local kernel transform is constructed for the classification of different diseases. The combination of the gene mining approach with support vector machine is demonstrated for cancer classification using two public data sets. The results reveal that significant genes are identified for each cancer, and the classification model shows satisfactory performance in training and prediction for both data sets.     

Simultaneous chemiluminescence determination of promazine and fluphenazine using support vector regression

In this work, chemiluminescence (CL) behaviors of two selected phenothiazines, namely promazine and fluphenazine hydrochloride, were investigated for their simultaneous determination using oxidation of Ru(bipy)₃²⁺ by Ce⁴⁺ ions in acidic media. This method is based on the kinetic distinction of the CL reactions of fluphenazine and promazine with Ru(bipy)₃²⁺ and Ce⁴⁺ system in a sulfuric acid medium. Least square support vector regression models were constructed for relating concentrations of both compounds to their CL profiles. The parameters of the model consisting of σ² and γ were optimized using all possible combinations of σ² and γ to select the model with the minimum root mean square cross validation. Under optimized conditions, the univariate calibration curve was linear over the concentration ranges of 0.4-30.0 μg mL⁻¹ and 0.07-5.0 μg mL⁻¹ with detection limits of 0.1 μg mL⁻¹ and 0.04 μg mL⁻¹ for promazine and fluphenazine, respectively. The influence of potential interfering substances on the determination of promazine and fluphenazine were studied. The proposed method was used for simultaneous determination of both compounds in synthetic mixtures and in spiked human plasma.     

Classification and differential metabolite discovery of liver diseases based on plasma metabolic profiling and support vector machines

Discovery of differential metabolites is the focus of metabonomics study. It has very important applications in pathogenesis and disease classification. The aim of this work is to identify differential metabolites for classifying the patients with hepatocellular carcinoma, cirrhosis and hepatitis based on metabolic profiling data analyzed by gas chromatography-time of flight mass spectrometry. A two-stage feature selection algorithm, F-SVM, combining F-score in analysis of variance and support vector machine (SVM), was applied in discovering discriminative metabolites for three different types of liver diseases. The results show that the accuracy rate of the double cross-validation was 73.68±2.98%. 22 important differential metabolites selected by F-SVM were identified and related pathophysiological process of liver diseases was set forth. We conclude that F-SVM is quite feasible to be applied in the selection of biologically relevant features in metabonomics.     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety.The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes.The clinical prototype consists of three parts:the laser beam,photodetectors,and the data acquisition system.The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber.When the bacterial microbes pass by the lase...     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis,E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.     

Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine

Protein methylation is involved in dozens of biological processes and plays an important role in adjusting protein physicochemical properties, conformation and function. However, with the rapid increase of protein sequence entering into databanks, the gap between the number of known sequence and the number of known methylation annotation is widening rapidly. Therefore, it is vitally significant to develop a computational method for quick and accurate identification of methylation sites. In this study, a novel predictor (Methy_SVMIACO) based on support vector machine (SVM) and improved ant colony optimization algorithm (IACO) is developed to identify methylation sites. The IACO is utilized to find the optimal feature subset and parameter of SVM, while SVM is employed to perform the identification of methylation sites. Comparison of the IACO with conventional ACO shows that the IACO converges quickly toward the global optimal solution and it is more useful tool for feature selection and SVM parameter optimization. The performance of Methy_SVMIACO is evaluated with a sensitivity of 85.71%, a specificity of 86.67%, an accuracy of 86.19% and a Matthew's correlation coefficient (MCC) of 0.7238 for lysine as well as a sensitivity of 89.08%, a specificity of 94.07%, an accuracy of 91.56% and a MCC of 0.8323 for arginine in 10-fold cross-validation test. It is shown through the analysis of the optimal feature subset that some upstream and downstream residues play important role in the methylation of arginine and lysine. Compared with other existing methods, the Methy_SVMIACO provides higher Acc, Sen and Spe, indicating that the current method may serve as a powerful complementary tool to other existing approaches in this area. The Methy_SVMIACO can be acquired freely on request from the authors.