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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]

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]

An issue for class‐imbalanced learning is what assessment metric should be employed. So far, precision‐recall curve (PRC) as a metric is rarely used in practice as compared with its alternative of receiver operating characteristic (ROC). This study investigates the performance of PRC as the evaluating criterion to address the class‐imbalanced data and focuses on the comparison of PRC with ROC. The advantages of PRC over ROC on assessing class‐imbalanced data are also investigated and tested on our proposed algorithm by tuning the whole model parameters in simulation studies and real data examples. The result shows that PRC is competitive with ROC as performance measurement for handling class‐imbalanced data in tuning the model parameters. PRC can be considered as an alternative but effective assessment for preprocessing (such as variable selection) skewed data and building a classifier in class‐imbalanced learning. [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]

Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally different, and this inconsistency among different variable ranking methods is usually ignored in practice. To address this problem, we propose a simple strategy called rank aggregation with re-balance (RAR) for finding key variables from class-imbalanced data. RAR fuses each rank to generate a synthetic rank that takes every ranking into account. The class-imbalanced data are modified via different re-sampling procedures, and RAR is performed in this balanced situation. Five class-imbalanced real datasets and their re-balanced ones are employed to test the RAR's performance, and RAR is compared with several popular feature screening methods. The result shows that RAR is highly competitive and almost better than single filtering screening in terms of several assessing metrics. Performing re-balanced pretreatment is hugely effective in rank aggregation when the data are class-imbalanced. [ABSTRACT FROM AUTHOR]

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 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]

Background: Early diagnosis of hepatocellular carcinoma may help to ensure that patients have a chance for long-term survival; however, currently available biomarkers lack sensitivity and specificity., Aim: To characterize the serum metabolome of hepatocellular carcinoma in order to develop a new metabolomics diagnostic model and identifying novel biomarkers for screening hepatocellular carcinoma based on the pattern recognition method., Methods: Ultra-performance liquid chromatography-mass spectroscopy was used to characterize the serum metabolome of hepatocellular carcinoma ( n = 30) and cirrhosis ( n = 29) patients, followed by sequential feature selection combined with linear discriminant analysis to process the multivariate data., Results: The concentrations of most metabolites, including proline, were lower in patients with hepatocellular carcinoma, whereas the hydroxypurine levels were higher in these patients. As ordinary analysis models failed to discriminate hepatocellular carcinoma from cirrhosis, pattern recognition analysis was used to establish a pattern recognition model that included hydroxypurine and proline. The leave-one-out cross-validation accuracy and area under the receiver operating characteristic curve analysis were 95.00% and 0.90 [95% Confidence Interval (CI): 0.81-0.99] for the training set, respectively, and 78.95% and 0.84 (95%CI: 0.67-1.00) for the validation set, respectively. In contrast, for α-fetoprotein, the accuracy and area under the receiver operating characteristic curve were 65.00% and 0.69 (95%CI: 0.52-0.86) for the training set, respectively, and 68.42% and 0.68 (95%CI: 0.41-0.94) for the validation set, respectively. The Z test revealed that the area under the curve of the linear discriminant analysis model was significantly higher than the area under the curve of α-fetoprotein ( P < 0.05) in both the training and validation sets., Conclusion: Hydroxypurine and proline might be novel biomarkers for hepatocellular carcinoma, and this disease could be diagnosed by the metabolomics model based on pattern recognition., Competing Interests: Conflict-of-interest statement: The authors have declared that no competing interests exist., (©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.)

Accurate prediction of peptide fragment ion mass spectra is one of the critical factors to guarantee confident peptide identification by protein sequence database search in bottom-up proteomics. In an attempt to accurately and comprehensively predict this type of mass spectra, a framework named MS(2)PBPI is proposed. MS(2)PBPI first extracts fragment ions from large-scale MS/MS spectra data sets according to the peptide fragmentation pathways and uses binary trees to divide the obtained bulky data into tens to more than 1000 regions. For each adequate region, stochastic gradient boosting tree regression model is constructed. By constructing hundreds of these models, MS(2)PBPI is able to predict MS/MS spectra for unmodified and modified peptides with reasonable accuracy. Moreover, high consistency between predicted and experimental MS/MS spectra derived from different ion trap instruments with low and high resolving power is achieved. MS(2)PBPI outperforms existing algorithms MassAnalyzer and PeptideART.

To discover novel lung adenocarcinoma (AdC) biomarkers, isobaric tags for relative and absolute quantitation (iTRAQ)-tagging combined with 2D-LC-MS/MS analysis was used to identify differentially expressed plasma membrane proteins in lung AdC and paired paraneoplastic normal lung tissues (PNLTs) adjacent to tumors. In this study, significant caveolin-1 downregulation and integrin β1 upregulation was observed in primary lung AdC vs. PNLT. As there has been no report on the association of integrin β1 with lung AdC, immunohistochemical staining was performed to detect the expression of integrin β1 in an independent set of archival tissue specimens including 42 cases of PLNT, 46 cases of without lymph node metastasis primary AdC (non-LNM AdC) and 62 cases of LNM AdC; the correlation of their expression levels with clinicopathological characteristics and clinical outcomes were evaluated. Based on the data, upregulation of integrin β1 was significantly correlated with advanced clinical stage and lymph node metastasis. Integrin β1 overexpression was significantly associated with advanced clinical stage (P<0.05), lymph node metastasis (P<0.05), increased relapse rate (P<0.05) and decreased overall survival (P<0.05) in AdCs. Cox regression analysis indicated that integrin β1 overexpression is an independent prognostic factor. The data suggest that integrin β1 is a potential biomarker for LNM and prognosis of AdC and integrin β1 upregulation may play an important role in the pathogenesis of AdC.

Traditional Chinese medicines (TCMs) are getting more and more popular nowadays in the whole world for improving health condition of human beings as well as preventing and healing diseases. TCM is a multi-component system with components mostly unknown, and only a few compounds are responsible for the pharmaceutical and/or toxic effects. The large numbers of other components in the TCM make the screening and analysis of the bioactive components extremely difficult. So, separation and analysis of the desired chemical components in TCM are very important subjects for modernization research of TCM. Thus, many novel separation techniques with significant advantages over conventional methods were introduced and applied to separation and analysis of the chemical constituents in TCM. This review presents just a brief outline of the applications of different separation methods for the isolation and analysis of TCM constituents.

The rapid development of systems biology and especially the advances in the high-throughput and comprehensive research technologies and research idea in metabonomics provide new strategies in the analysis of active components in the formula of traditional Chinese medicine (TCM) and pharmacokinetic analysis of their metabolites in vivo. Furthermore, the initiatives of metabonomics may pave a new way to explain the action mode of TCM in the light of modern sciences, while the metabonomic research achievements may contribute to the establishment of a new technique platform for evaluating the efficacy of the formula of TCM.