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Imbalanced domain prediction analysis is currently one of the hot research topics. Many real‐world data mining analyses involve using imbalanced data to obtain predictive models. In the context of imbalance, research on classification problems has been extensive, but research on regression problems is negligible. Rare values rarely occur in imbalanced regression problems, but the focus is on accurately predicting the continuous target variables of rare instances. One of the challenges in imbalanced regression is finding a suitable strategy to rebalance the original dataset in order to improve the predictive performance of the model in rare instances. In this study, two algorithms are proposed: sigma nearest over‐sampling based on convex combination for regression (SNOCCR) and affine combination‐based over‐sampling (ACOS). ACOS rebalances the original dataset by generating new instances through the affine combinations of the original examples. The region where the new instances are generated can be adjusted based on the distribution of the data, ensuring that the generated cases better mimic the distribution of the original examples. The comparison among ACOS, SNOCCR, and other preprocessing methods was conducted on 15 datasets to validate the predictive performance of models trained on rebalanced datasets for rare instances. The experimental results indicate that ACOS outperforms other existing methods. In this study, two over‐sampling algorithms are proposed: sigma nearest over‐sampling based on convex combination for regression (SNOCCR) and affine combination‐based over‐sampling (ACOS) in order to improve the predictive performance of the model in imbalanced regression. The experimental results indicate that ACOS outperforms other existing methods. [ABSTRACT FROM AUTHOR]

Solvent system selection is the first step toward a successful counter-current chromatography (CCC) separation. This paper introduces a systematic and practical solvent system selection strategy based on the nonrandom two-liquid segment activity coefficient (NRTL-SAC) model, which is efficient in predicting the solute partition coefficient. Firstly, the application of the NRTL-SAC method was extended to the ethyl acetate/ n -butanol/water and chloroform/methanol/water solvent system families. Moreover, the versatility and predictive capability of the NRTL-SAC method were investigated. The results indicate that the solute molecular parameters identified from hexane/ethyl acetate/methanol/water solvent system family are capable of predicting a large number of partition coefficients in several other different solvent system families. The NRTL-SAC strategy was further validated by successfully separating five components from Salvia plebeian R.Br. We therefore propose that NRTL-SAC is a promising high throughput method for rapid solvent system selection and highly adaptable to screen suitable solvent system for real-life CCC separation. [ABSTRACT FROM AUTHOR]

Abstract: Tangerine peels are herbal materials of two coupled traditional Chinese medicines, Pericarpium Citri Reticulatae (PCR) and Pericarpium Citri Reticulatae Viride (PCRV). In this paper, high-performance liquid chromatographic fingerprints of tangerine peels during growth were firstly measured for deliberately collected 34 samples from three species (Citrus reticulata ‘Chachi’, Citrus reticulata ‘Dahongpao’ and Citrus erythrosa Tanaka). After sixteen characteristic components which have similar change trends in the grown process were screened out with the help of heuristic evolving latent projection (HELP) method, score plots of principal component analysis (PCA) successfully presented the grown footprints of tangerine peels. It implied that July might be the best harvest time for PCRV, November and December were better for PCR. Furthermore, hesperidin, nobiletin and tangeretin were screened as chemical markers by loadings of PCA. The HPLC–HELP–PCA strategy has shown its potential in optimization of harvest time and chemical markers’ screening, which will have wide perspective in the analysis of “coupled TCMs”. [Copyright &y& Elsevier]

Abstract: High-performance liquid chromatographic (HPLC) fingerprints of Pericarpium Citri Reticulatae (PCR) and Pericarpium Citri Reticulatae Viride (PCRV) were firstly measured for deliberately collected 39 authentic samples and 21 commercial samples. Both correlation coefficients of similarity for chromatograms and absolute peak areas of characteristic compounds were calculated for quantitative expression of the HPLC fingerprints. After principal component analysis (PCA) successfully distinguished the ‘mixed peels’ samples from authentic samples, partial least squares-linear discrimination analysis (PLS-LDA) was then effectively applied to class separation between authentic PCR and PCRV. Furthermore, the unequivocally determined compounds, hesperidin, nobiletin and tangeretin, were screened out by loadings plots of PCA and PLS-LDA. The results indicated that they could be used as chemical markers for discrimination among different groups of samples. The proposed method shows an efficient strategy for quality control of PCR and PCRV, which cannot only distinguish the ‘mixed peels’ but also discriminate authentic PCR and PCRV. This method has potential perspective for quality control of traditional Chinese medicine (TCM). [Copyright &y& Elsevier]

Abstract: Metabolic profiling has increasingly been used as a probe in disease diagnosis and pharmacological analysis. Herein, plasma fatty acid metabolic profiling including non-esterified fatty acid (NEFA) and esterified fatty acid (EFA) was investigated using gas chromatography/mass spectrometry (GC/MS) followed by multivariate statistical analysis. Partial least squares-linear discrimination analysis (PLS-LDA) model was established and validated to pattern discrimination between type 2 diabetic mellitus (DM-2) patients and health controls, and to extract novel biomarker information. Furthermore, the PLS-LDA model visually represented the alterations of NEFA metabolic profiles of diabetic patients with abdominal obesity in the treated process with rosiglitazone. The GC/MS-PLS-LDA analysis allowed comprehensive detection of plasma fatty acid, enabling fatty acid metabolic characterization of DM-2 patients, which included biomarkers different from health controls and dynamic change of NEFA profiles of patients after treated with medicine. This method might be a complement or an alternative to pathogenesis and pharmacodynamics research. [Copyright &y& Elsevier]

Feature selection and rebalancing can be seen as two preprocessing ways in class‐imbalanced learning. Recently, there have been many research achievements and applications on LASSO‐type feature selection, whereas most of them are not directly designed for addressing class‐imbalanced data. In this study, we proposed a LASSO‐based stable feature selection algorithm for class‐imbalanced data analysis, and false‐positive selection (FPS) under balanced and imbalanced situations was calculated via selection frequency of each predictor in doing stable selection. The results on simulation studies and real data examples show that class imbalance contributes to avoid overselection caused by LASSO when the data are highly correlated and a lower FPS can be obtained with class‐imbalanced data than balanced one in most of cases in the same settings. A statistical explanation was given for this phenomenon. In addition, it does not need to rebalance the class‐imbalanced data for performing such LASSO‐based feature selection with a stable strategy, and to some degree, intentionally disequilibrating the balanced data could be an alternative strategy to weaken overselection and to perform biomarker identification for finding a few of most important biomarkers. A LASSO‐based stable feature selection algorithm was proposed for imbalanced data. It is not necessary to rebalance the imbalanced data with such stable feature selection strategy. Class imbalance even contributes to avoid overselection caused by LASSO when data are strongly correlated. [ABSTRACT FROM AUTHOR]

Abstract: The importance of 5′AMP-activated protein kinase (AMPK) in regulating glucose and fatty acid metabolism is increasing. Thus, it is regarded as a new pharmacological target for treatment of obesity, insulin resistance and type 2 diabetes mellitus (T2DM). In order to explore the relationships between AMPK and diabetes mellitus, urines samples from four groups of C57 mice, i.e., the normal male and female C57 mice, female C57-AMPK gene knocked-out mice, and male C57-AMPK gene knocked-out mice, were studied by coupling GC/MS with a powerful machine learning method, random forest. The experimentation has been designed as two steps: firstly, the normal male and female mice were compared with male and female C57-AMPK gene knocked-out mice, respectively; then the differences between male C57-AMPK gene knocked-out mice and female C57-AMPK gene knocked-out mice were further detected. Finally, not only the differences between the normal C57 mice and C57-AMPK gene knocked-out mice were observed, but also the gender-related metabolites differences of the C57-AMPK gene knocked-out mice were obviously visualized. The results obtained with this research demonstrate that combining GC/MS profiling with random forest is a useful approach to analyze metabolites and to screen the potential biomarkers for exploring the relationships between AMPK and diabetes mellitus. [Copyright &y& Elsevier]

A comprehensive investigation was performed to understand the influence of sequence scrambling in peptide ions on peptide identification results. To achieve this, four tandem mass spectrometry datasets with scrambled ions included and with them excluded were analyzed by Crux, X!Tandem, SpectraST, Lutefisk, and PepNovo. While the different algorithms differed in their performance, an increase in the number of correctly identified peptides was generally observed when removing scrambled ions, with the exception of the SpectraST algorithm. However, the variation of the match scores upon removal was unpredictable. Following these investigations, an interpretation was given on how the scrambled ions affect peptide identification. Lastly, a simulated theoretical mass spectral library derived from the NIST peptide Libraries was constructed and searched by SpectraST to study whether scrambled ions in predicted mass spectra could affect peptide identification. Consistent with the peptide library search results, no significant variations for dot product scores as well as peptide identification results were observed when these ions were included in the theoretical MS/MS spectra. From the five adopted algorithms, the SpectraST and Crux provided the most robust results, whereas X!Tandem, PepNovo, and Lutefisk were sensitive to the existence of the scrambled ions, especially the latter two de novo sequencing algorithms. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Scrambled ions have become the focus of recent investigations of peptide fragmentation. Here, an investigation of more than 390,000 high quality CID mass spectra is presented to explore the extent of scrambled ions in mass spectra and the possible fragmentation rules during scramble reactions. For the former, scrambled ions generally make up more than 10 % of mass spectra in number, although the abundances are less than 0.1 of the base peak. For the latter, relatively preferential re-opening sites were found for aliphatic residues Ala, Ile, Leu, and other residues such as Met, Gln, Ser, Phe, and Thr, whereas disfavored sites were found for basic residues Arg, Lys, and His, and Trp for both scrambled b and a ions. Similar preferential order in re-opening reaction was found in the reaction of losing internal residues when cleavage occurs at C-terminal side of 20 residues. However, when cleavage occurs at N-terminal side, Glu, Phe, and Trp become the most preferential sites. These results provide a deep insight into cleavage rules during scramble reactions for prediction of peptide mass spectra. Also, an additional investigation of whether scrambled ions could help discriminate false identifications from correct identifications was performed. Probing the number fraction of scrambled ions in falsely and correctly interpreted spectra and analyzing the correlation between scrambled ions and SEQUEST scores XCorr and Sp showed scrambled ions could at some extent help improve the discrimination in singly charged identifications, whereas no improvement was found for multiply charged results. [ABSTRACT FROM AUTHOR]

Abstract: Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex, which brings an enormous amount of challenges to existing statistical modeling. Thus there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In the work, we developed a novel kernel Fisher discriminant analysis (KFDA) algorithm by constructing an informative kernel based on decision tree ensemble. The constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement space. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building kernel. Moreover, KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some extent. Finally, two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches. [Copyright &y& Elsevier]

Elastic net (Enet) and sparse partial least squares (SPLS) are frequently employed for wavelength selection and model calibration in analysis of near infrared spectroscopy data. Enet and SPLS can perform variable selection and model calibration simultaneously. And they also tend to select wavelength intervals rather than individual wavelengths when the predictors are multicollinear. In this paper, we focus on comparison of Enet and SPLS in interval wavelength selection and model calibration for near infrared spectroscopy data. The results from both simulation and real spectroscopy data show that Enet method tends to select less predictors as key variables than SPLS; thus it gets more parsimony model and brings advantages for model interpretation. SPLS can obtain much lower mean square of prediction error (MSE) than Enet. So SPLS is more suitable when the attention is to get better model fitting accuracy. The above conclusion is still held when coming to performing the strongly correlated NIR spectroscopy data whose predictors present group structures, Enet exhibits more sparse property than SPLS, and the selected predictors (wavelengths) are segmentally successive. [ABSTRACT FROM AUTHOR]

The peak shifts may lead to an incorrect statistical result for nontargeted metabolomics profiling, such as classification and discrimination in pattern recognition. In the paper, a more accurate alignment algorithm is developed based on Subwindow Factor Analysis and Mass Spectral information (SFA-MS). Compared with other methods, this new algorithm aligns the peaks more accurately without changing their shapes, especially for the overlapping peak clusters. To begin, the Continuous Wavelet Transform with Haar wavelet as the mother wavelet (Haar CWT) is used to determine the position and width of peaks. On this basis, the candidate drift points are confirmed by Fast Fourier Transform (FFT) cross correlation. Furthermore, the MS fitting degree of the common components between the reference chromatogram and the raw chromatogram is determined by the Subwindow Factor Analysis (SFA). When the MS information between reference and raw peaks is identical, the corresponding moving points are the optimum shifts. It is remarkable that all the peaks are moved through linear interpolation in the non-peak parts, so that the aligned chromatograms remain unchanged. The SFA-MS algorithm was implemented in the Matlab language and is available as an open source package. [ABSTRACT FROM AUTHOR]

Screening important variables for class-imbalanced data is still a challenging task. In this study, we propose an algorithm for stably selecting key variables on class-imbalanced data based on the precision-recall curve (PRC), where the PRC is utilized as the assessment criterion in the model building stage, and sparse regularized logistic regression combined with subsampling (SRLRS) is designed to perform stable variable selection. Considering the characteristic of class-imbalanced data, we also proposed classification-based partition for cross validation, as well as leaving half of majority observations out and leaving one minority observation out (LHO-LOO) for subsampling. Simulation results and real data showed that our algorithm is highly suitable for handling class-imbalanced data, and that the PRC can be an alternative evaluation criterion for model selection when handling class-imbalanced data. [ABSTRACT FROM AUTHOR]

The partial least squares-discriminant analysis (PLS-DA) is the most widely used statistical tool to perform classification and biomarker screening in metabolomics. However, the PLS-DA tends to overfit the data, and the selection of biomarkers is often unstable because of the disturbance of uninformative variables in principal components. In this paper, we propose an algorithm for performing stable biomarker screening and for seeking the optimal generalization performance, in which the biomarker identification is based on sparse regularization variable selection in combination with subsampling (SRS), and the classification is subsequently performed by a linear support vector machine (SVM) classifier in the selected-variable space to obtain the maximum classification accuracy. Two metabolomics datasets measured by gas chromatography-mass spectrometry are employed to evaluate the performance of the proposed SRS-SVM algorithm, and the comparison with existing related algorithms is given. The result shows that the SRS-SVM algorithm outperforms the PLS-DA and is competitive with other related algorithms in terms of prediction classification accuracy measured by both internal and external validation. Furthermore, the selection of candidate biomarkers is quite stable by the SRS-SVM algorithm, and it can be an alternative and competitive method for the analysis of metabolomics data. The R code for implementing the SRS-SVM algorithm is available in the Electronic supplementary material . [ABSTRACT FROM AUTHOR]

Main target of metabolomics research is to reveal informative metabolites or biomarkers, which can be considered as a process of variable selection. So far, several methods, such as regression coefficient (RC), weights or variable importance in projection (VIP), have been widely used to assess the variable importance when building the partial least squares linear discriminant analysis PLS-LDA classification model. Then a set of metabolites can be selected by fixing a threshold value considering the rank of metabolites. However, they do not take into account the combination effect among a subset of variables, which will lead to bias within the results. In this work, a strategy named as variable importance analysis based on random variable combination (VIAVC), is developed for statistical assessment of variable importance. The framework of VIAVC includes mainly three parts: (1) employ a novel variables sampling method, called binary matrix resampling, which can guarantee that each variable has been selected with the same probability and generate a population of different variable combinations; (2) the importance of each variable is assessed by percent decrease or increase of the area under the receiver operating characteristic curve when the variable is excluded for the modeling by PLS-LDA; (3) iteratively retain and output the rank of the final remaining informative variables. The results of the applications to three metabolic datasets illustrate that VIAVC has better performance compared with other methods including RC, VIP and subwindow permutation analysis. The MATLAB code for implementing VIAVC is available in the supplemental materials. [ABSTRACT FROM AUTHOR]

Partial least squares (PLS) is one of the most widely used methods for chemical modeling. However, like many other parameter tunable methods, it has strong tendency of over-fitting. Thus, a crucial step in PLS model building is to select the optimal number of latent variables (nLVs). Cross-validation (CV) is the most popular method for PLS model selection because it selects a model from the perspective of prediction ability. However, a clear minimum of prediction errors may not be obtained in CV which makes the model selection difficult. To solve the problem, we proposed a new strategy for PLS model selection which combines the cross-validated coefficient of determination ( Q c v 2 ) and model stability ( S ). S is defined as the stability of PLS regression vectors which is obtained using model population analysis (MPA). The results show that, when a clear maximum of Q c v 2 is not obtained, S can provide additional information of over-fitting and it helps in finding the optimal nLVs. Compared with other regression vector based indictors such as the Euclidean 2-norm ( B2 ), the Durbin Watson statistic ( DW ) and the jaggedness ( J ), S is more sensitive to over-fitting. The model selected by our method has both good prediction ability and stability. [ABSTRACT FROM AUTHOR]

Bioactive component identification is a crucial issue in search for new drug leads. We provide a new strategy to search for bioactive components based on Sure Independence Screening (SIS) and interval PLS (iPLS). The method, which is termed as SIS–iPLS, is not only able to find out the chief bioactive components, but also able to judge how many components should be there responsible for the total bioactivity. The method is totally “data-driven” with no need for prior knowledge about the unknown mixture analyzed, therefore especially suitable for effect-directed work like bioassay-guided fractionation. Two data sets, a synthetic mixture system of twelve components and a suite of Radix Puerariae Lobatae extracts samples, are used to test the identification ability of the SIS–iPLS method. [ABSTRACT FROM AUTHOR]

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: URL URL="HTTPS://SOURCEFORGE.NET/PROJECTS/MULTIVARIATE-ANALYSIS/FILES/VISSA/"https://sourceforge.net/projects/multivariate-analysis/files/VISSA//URL. [ABSTRACT FROM AUTHOR]

Highlights: [•] An effective approach has been developed for analysis complex metabolomics data. [•] Random forest provides useful tools for data visualization and interpretation. [•] Metabolic perturbations in metabolic syndrome patients were discovered. [ABSTRACT FROM AUTHOR]

Abstract: One of the main goals of metabolomics studies is to discover informative metabolites or biomarkers, which may be used to diagnose diseases and to find out pathology. Sophisticated feature selection approaches are required to extract the information hidden in such complex ‘omics’ data. In this study, it is proposed a new and robust selective method by combining random forests (RF) with model population analysis (MPA), for selecting informative metabolites from three metabolomic datasets. According to the contribution to the classification accuracy, the metabolites were classified into three kinds: informative, no-informative, and interfering metabolites. Based on the proposed method, some informative metabolites were selected for three datasets; further analyses of these metabolites between healthy and diseased groups were then performed, showing by T-test that the P values for all these selected metabolites were lower than 0.05. Moreover, the informative metabolites identified by the current method were demonstrated to be correlated with the clinical outcome under investigation. The source codes of MPA-RF in Matlab can be freely downloaded from http://code.google.com/p/my-research-list/downloads/list [Copyright &y& Elsevier]

Abstract: The volatile components between stems and roots and also among five Clematis species from China were studied and analyzed by gas chromatography–mass spectrometry (GC–MS) combined with alternative moving window factor analysis (AMWFA), a new chemometric resolution method. Identification of the compounds was also assisted by comparison of temperature-programmed retention indices (PTRIs) on HP-5MS with authentic samples included in our own laboratory database under construction. A total of 153 different compounds accounting for 86.6–96.5% were identified and significant qualitative and quantitative differences were observed among the samples. The major volatile components in different essential oils from Clematis species were n-hexadecanoic acid and (Z,Z)-9,12-octadecadienoic acid. Our work further demonstrated chemometric resolution techniques upon the two-dimensional data and PTRIs can provide a complementary and convenient method for fast and accurate analysis of complex essential oils. [Copyright &y& Elsevier]

Abstract: A sensitive liquid chromatography–electrospray ionization mass spectrometry (LC–ESI-MS) method is developed and validated for rapid determination of amantadine in human plasma. Desloratadine was used as the internal standard (I.S.). Human plasma (0.2mL) was first alkalified with 100μL of sodium hydroxide (3M) and then extracted with 1mL of n-hexane containing 1% isopropanol (v/v) and 10% dichloromethane (v/v) by vortex-mixer for 3min. The mixture was centrifuged at 14,000rpm for 5min. The supernatant was evaporated to dryness and the residue was dissolved in mobile phase. Samples were separated using a Thermo Hypersil-HyPURITYC18 reversed-phase column (150mm×2.1mm i.d., 5μm). Mobile phase consisted of methanol–acetonitrile–20mM ammonium acetate (45:10:45, v/v/v) containing 1% acetic acid with pH 4.0. Amantadine and I.S. were measured by electrospray ion source in positive selective ion monitoring mode. The good linearity ranged from 3.9 to 1000ng/mL and the lowest limit of quantification was 3.9ng/mL. The extraction efficiencies were approximately 70% and recoveries of method ranged from 98.53 to 103.24%. The intra-day relative standard deviations (R.S.D.) were less than 8.43% and inter-day R.S.D. below 10.59%. The quality control samples were stable when kept at room temperature for 12h, at −20°C for 30 days and after four freeze/thaw cycles. The method has been successfully used to evaluation of the pharmacokinetics and bioequivalence of amantadine in 20 healthy volunteers after an oral dose of 100mg amantadine. [Copyright &y& Elsevier]

Learning from class-imbalanced data has gained increasing attention in recent years due to the massive growth of skewed data across many scientific fields such as metabolomics. Some researches show that it is not the imbalance itself which hinders the classification performance, but class overlapping do play an important role in the performance degradation when associated to class-imbalance. So alleviating the overlapping of the imbalanced data might be an effective way to improve the performance in class-imbalance learning. In this study, we propose two feature selection algorithms that aim to minimize the overlap degree between the majority and the minority, which is based on a simple assumption that decreasing overlap degree of a data set makes it more separable. The proposed MOSNS and MOSS methods are built via sparse regularization techniques. Simulation results indicate that our algorithms is effective in recognizing key features and control false discoveries for class-imbalance learning. Four class-imbalanced metabolomics data sets are also employed to test the performance of our algorithm, and a comparison with accuracy (ACC)-based and ROC-based selection procedures is performed. The result shows that our algorithms are highly competitive and can be an alternative feature selection strategy in class-imbalance learning. ● Two feature selection algorithms are proposed by minimizing the overlap of a data set. ● Sparse feature selection strategy are used in class-imbalance learning. ● The classification performance of metabolomics data can be improved by sparse feature selection and re-balance preprocessing. [ABSTRACT FROM AUTHOR]