Smoothly Clipped Absolute Deviation Research Articles

A trajectory data-driven approach for traffic risk prediction: incorporating variable interactions and pre-screening

ABSTRACT Although historical crash data and trajectory data have been widely applied to crash and risk predictions, both types of data have their own limitations. As a solution, this study investigates the impact of the traffic flow parameters and their interaction terms on risk prediction performance, employing a variable pre-screening approach (i.e. Smoothly Clipped Absolute Deviation (SCAD)). A research framework is proposed for more efficient risk prediction, and a detailed case study is further conducted using the proposed approach. In the case study, real vehicle trajectory data from HighD are processed and used, which can be aggregated to extract both traffic flow parameters and corresponding risk data during a specific time interval. As for the risk detection, Time-to-Collision (TTC) index is utilized to identify risky conditions. For different lanes (i.e. inner, middle and outer lanes), the impact of variables, including interaction terms, on risk is explored using the SCAD-logistic models. Furthermore, machine learning methods are employed to compare the risk prediction performance before and after considering interaction terms, as well as before and after variable pre-screening. Finally, the superiority of the machine learning models after SCAD-based variable pre-screening is demonstrated. Results indicate that the interaction terms between traffic flow parameters have significant impacts on the traffic risk. Besides, considering interaction terms and variable pre-screening can improve risk prediction accuracy. Furthermore, the proposed models outperform Random Forest (RF) in terms of predicting traffic risk, achieving a maximum 21.24% accuracy improvement and reducing computational time by up to 31.51%. Findings of this study are expected to contribute to the high-precision prediction of real-time risk in the future.

Urinary volatile organic compounds (VOCs) based prostate cancer diagnosis via high-dimensional classification

Early detection of prostate cancer is critical for successful treatment and survival. However, current diagnostic methods such as prostate-specific antigen (PSA) testing and digital rectal examination (DRE) have limitations in accuracy, specificity, and sensitivity. Recent research suggests that urinary volatile organic compounds (VOCs) could serve as potential biomarkers for prostate cancer diagnosis. In this study, urine samples from 337 PCa-positive and 233 PCa-negative patients were collected to develop a diagnosis model. The study involves a high dimensional (HD) classification problem due to the vast number of measured VOCs. Our findings reveal that regularized logistic regression outperforms numerous other classifiers when analyzing the collected data. In particular, we have selected a regularized logistic model with the SCAD (smoothly clipped absolute deviation) penalty as the final model, which attains an AUC (area under the ROC curve) of 0.748, in contrast to a PSA-based AUC of 0.540. These results underscore the potential of VOC-based diagnosis as a clinically feasible approach for PCa screening.

Using Regularized Methods to Validate Q-Matrix in Cognitive Diagnostic Assessment

One of the central components of cognitive diagnostic assessment is the Q-matrix, which is an essential loading indicator matrix and is typically constructed by subject matter experts. Nonetheless, to a large extent, the construction of Q-matrix remains a subjective process and might lead to misspecifications. Many researchers have recognized the importance of estimating or validating the Q-matrix, but most of them focus on the conditions of relatively large sample sizes. This article aims to explore Q-matrix validation possibilities under small sample conditions and uses regularized methods to validate the Q-matrix based on the compensatory reparametrized unified model and generalized deterministic inputs, noisy “and” gate models. Simulation studies were conducted to evaluate the viability of the modified least absolute shrinkage and selection operator (Lasso) and modified smoothly clipped absolute deviation (SCAD) methods, comparing them with existing methods. Results show that the modified Lasso and the modified SCAD methods outperform the stepwise, Hull, and MLR-B methods in general, especially under the conditions of small sample sizes. While good recovery in all small sample size conditions is not guaranteed, the modified methods demonstrate advantages across various item quality conditions. Also, a real data set is analyzed to illustrate the application of the modified methods.

Penalized deep partially linear cox models with application to CT scans of lung cancer patients.

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (eg, image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of dimensionality. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the smoothly clipped absolute deviation (SCAD) penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes.

Newton-Raphson Meets Sparsity: Sparse Learning Via a Novel Penalty and a Fast Solver.

In machine learning and statistics, the penalized regression methods are the main tools for variable selection (or feature selection) in high-dimensional sparse data analysis. Due to the nonsmoothness of the associated thresholding operators of commonly used penalties such as the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD), and the minimax concave penalty (MCP), the classical Newton-Raphson algorithm cannot be used. In this article, we propose a cubic Hermite interpolation penalty (CHIP) with a smoothing thresholding operator. Theoretically, we establish the nonasymptotic estimation error bounds for the global minimizer of the CHIP penalized high-dimensional linear regression. Moreover, we show that the estimated support coincides with the target support with a high probability. We derive the Karush-Kuhn-Tucker (KKT) condition for the CHIP penalized estimator and then develop a support detection-based Newton-Raphson (SDNR) algorithm to solve it. Simulation studies demonstrate that the proposed method performs well in a wide range of finite sample situations. We also illustrate the application of our method with a real data example.

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin’s concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

Wavelet transform based image enhancement: A noise reduction approach

<span lang="EN-US">This paper introduces a new thresholding function that mixes the soft thresholding functions and Smoothly Clipped Absolute Deviation (SCAD) for denoising images using the decimated wavelet transform technique, which is widely popular in various applications. The proposed method is applied to denoise noisy images contaminated with additive white Gaussian noise, employing the Top rule. The efficiency of this new thresholding function is also evaluated within the context of the Translation Invariant method. The results are compared with existing methods such as SCAD, soft thresholding, and the Wiener filter-based denoising approach. Parameters such as Root Mean Square Error (RMSE) and Peak Signal Noise Ratio (PSNR) are employed to assess the quality of denoising.</span>

Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse

BackgroundData loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical diagnosis. A suitable data imputation method can help researchers make better use of valuable medical data.MethodsIn this paper, five popular imputation methods including mean imputation, expectation-maximization (EM) imputation, K-nearest neighbors (KNN) imputation, denoising autoencoders (DAE) and generative adversarial imputation nets (GAIN) are employed on an incomplete clinical data with 28,274 cases for vaginal prolapse prediction. A comprehensive comparison study for the performance of these methods has been conducted through certain classification criteria. It is shown that the prediction accuracy can be greatly improved by using the imputed data, especially by GAIN. To find out the important risk factors to this disease among a large number of candidate features, three variable selection methods: the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) and the broken adaptive ridge (BAR) are implemented in logistic regression for feature selection on the imputed datasets. In pursuit of our primary objective, which is accurate diagnosis, we employed diagnostic accuracy (classification accuracy) as a pivotal metric to assess both imputation and feature selection techniques. This assessment encompassed seven classifiers (logistic regression (LR) classifier, random forest (RF) classifier, support machine classifier (SVC), extreme gradient boosting (XGBoost) , LASSO classifier, SCAD classifier and Elastic Net classifier)enhancing the comprehensiveness of our evaluation.ResultsThe proposed framework imputation-variable selection-prediction is quite suitable to the collected vaginal prolapse datasets. It is observed that the original dataset is well imputed by GAIN first, and then 9 most significant features were selected using BAR from the original 67 features in GAIN imputed dataset, with only negligible loss in model prediction. BAR is superior to the other two variable selection methods in our tests.ConcludesOverall, combining the imputation, classification and variable selection, we achieve good interpretability while maintaining high accuracy in computer-aided medical diagnosis.

Building a cross-border e-commerce talent training platform based on logistic regression model

Student success is crucial to the process of building a Cross-border e-commerce (CBEC) talent development platform. Analysis of the important aspects impacting performance and performance prediction are carried out with the goal of enhancing students' academic outcomes. To better forecast student outcomes, a logistic regression model is used for factor analysis, and a penalty function is implemented. Parameters are reconciled using K-fold cross-validation, and then estimated using the coordinate descent technique. Model performance validation findings indicated that the Area Under the curve (AUC) for the minimax concave penalty (MCP) and smoothlyclippedabsolutedeviation(SCAD) penalized logistic regression models were 0.772 and 0.771, respectively. Both the MCP and SCAD penalized logistic regression models have overall accuracies of 0.738 and 0.739, respectively. Researchers found that for MCP, the correlation coefficient was 0.99949, and for SCAD, it was 0.99958, between the projected value and the anticipated value of students' performance. Due to their superior prediction accuracy, the MCP and SCAD penalized logistic regression models may be used as analytical tools in the development of the CBEC talent training platform.

The Role of Social Support and Socioeconomic Factors in the Prediction of Depression among First-Year Undergraduate Students.

This study was devoted to determining the role of social support and socioeconomic factors in predicting students' depression. In this cross-sectional study, all first-year undergraduate students in the Shahrekord University of Medical Sciences, Iran, during the 2019-2020 academic year were included via the census method. Data collection tools include a researcher-made checklist about demographic and socioeconomic status, a standard questionnaire of perceived social support, and Beck's depression questionnaire. Smoothly clipped absolute deviation (SCAD) linear regression was used to model the role of social support and socioeconomic factors in predicting depression. Out of the 220 first-year undergraduate students, 174 (79.1%) were female, and 176 (80.0%) were single. The mean ± SD of depression score among the first-year undergraduate students was 10.56 ± 5.19, and the mean ± SD of social support score was 48.86 ± 5.46. The mean score of depression was significantly higher in female students than in males (11.09 versus 8.59, P = 0.001) but was not statistically significant in different categories of age (P = 0.70), marital status (P = 0.37), ethnicity (P = 0.10), parents' education, and the other demographic variables. Pearson's correlation showed an inverse and significant correlation between depression and social support (R = -0.20, P = 0.003). The mean score of depression was at the highest level for students of public health and environmental health majors and was the lowest for students of laboratory sciences, which was statistically significant (P < 0.001). After adjusting the other variables, SCAD regression showed that social support plays a key role in depression prediction, and increasing social support leads to a decrease in depression score. Considering the existence of an inverse and significant correlation between depression and social support, any intervention to promote social support for first-year undergraduate students may decrease depression.

A penalized least product relative error loss function based on wavelet decomposition for non-parametric multiplicative additive models

In this paper, an estimation method based on wavelet decomposition is proposed for non-parametric multiplicative additive models. The proposed method can transform the estimation problem of non-parametric models into the estimation of parametric models without assuming the error distribution. To achieve the sparsity of the model, we apply the smoothly clipped absolute deviation (SCAD) penalty to estimate and select the variables based on the least product relative error loss function. Simulation results support the theoretical characteristics. We show that the proposed method is more effective than the traditional spline estimation method. Applications further show the performance of the proposed method with the actual data.

Chronic Obstructive Pulmonary Disease: Novel Genes Detection with Penalized Logistic Regression.

This study aimed to introduce novel techniques for identifying the genes associated with developing chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using regression methods. This is a secondary analysis of the data from an experimental study. We used penalized logistic regressions with three different types of penalties included least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP), and smoothly clipped absolute deviation (SCAD). The models were trained using genome-wide expression profiling to define gene networks relevant to the COPD stages. A 10-fold cross-validation scheme was used to evaluate the performance of the methods. In addition, we validate our results by the external validity approach. We reported the sensitivity, specificity, and area under curve (AUC) of the models. There were 21, 22, and 18 significantly associated genes for LASSO, SCAD, and MCP models, respectively. The most statistically conservative method (detecting less significant features) was MCP detected 18 genes that were all detected by the other two approaches. The most appropriate approach was a SCAD penalized logistic regression (AUC= 96.26, sensitivity= 94.2, specificity= 86.96). In this study, we have a common panel of 18 genes in all three models that show a significant positive and negative correlation with COPD, in which RNF130, STX6, PLCB1, CACNA1G, LARP4B, LOC100507634, SLC38A2, and STIM2 showed the odds ratio (OR) more than 1. However, there was a slight difference between penalized methods. Regularization solves the serious dimensionality problem in using this kind of regression. More exploration of how these genes affect the outcome and mechanism is possible more quickly in this manner. The regression-based approaches we present could apply to overcoming this issue.

Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method

&lt;p&gt;&lt;span lang="EN-US"&gt;Dengue fever is still a crucial public health problem in Indonesia, with the highest fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38. &lt;/span&gt;&lt;/p&gt;

Feature-specific inference for penalized regression using local false discovery rates.

Penalized regression methods such as the lasso are a popular approach to analyzing high-dimensional data. One attractive property of the lasso is that it naturally performs variable selection. An important area of concern, however, is the reliability of these selections. Motivated by local false discovery rate methodology from the large-scale hypothesis testing literature, we propose a method for calculating a local false discovery rate for each variable under consideration by the lasso model. These rates can be used to assess the reliability of an individual feature, or to estimate the model's overall false discovery rate. The method can be used for any level of regularization. This is particularly useful for models with a few highly significant features but a high overall false discovery rate, a relatively common occurrence when using cross validation to select a model. It is also flexible enough to be applied to many varieties of penalized likelihoods including generalized linear models and Cox regression, and a variety of penalties, including the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty. We demonstrate the validity of this approach and contrast it with other inferential methods for penalized regression as well as with local false discovery rates for univariate hypothesis tests. Finally, we show the practical utility of our method by applying it to a case study involving gene expression in breast cancer patients.

P154 A novel serum protein signature as biomarker for Inflammatory Bowel Disease: A diagnostic performance and prediction modelling study using data from two independent inception cohorts

Abstract Background The diagnosis of inflammatory bowel disease (IBD) is often delayed since existing biomarkers in blood are imprecise and faecal tests are poorly accepted by patients. Therefore, we aimed to identify a diagnostic serum protein signature and validate its performance and utility. Methods Proximity extension immunoassay (PEA) methodology (Olink Proteomics, Uppsala, Sweden) was used to measure 184 immune and inflammatory markers in serum samples from adult patients with suspected IBD in the Swedish inception cohort SIC IBD (discovery cohort, n=451) and the Norwegian population-based inception cohort IBSEN III (validation cohort, n=554). Samples were obtained at presentation, i.e., almost all patients were treatment naïve. Diagnostic assessment was performed following the ECCO guidelines. Supervised machine learning with smoothly clipped absolute deviation (SCAD) regularised logistic regression and random forest (RF) models were employed in nested cross-validation to identify a diagnostic signature of IBD. Results The discovery cohort included 310 IBD patients and 141 symptomatic controls without any discernible evidence of IBD, and the validation cohort included 354 IBD patients and 200 symptomatic controls (Table 1). The regularised regression identified a signature of 26 proteins that distinguished IBD from symptomatic controls with an area under the curve (AUC) of 0.78 in the discovery cohort. Contributions of individual proteins for the diagnostic signature are shown in Figure 1. Next, we validated the diagnostic capacity of the protein signature in the validation cohort (AUC=0.82) and compared it to high-sensitivity C-reactive protein (hsCRP) (Figure 2, AUC=0.69, p=1.1×10-7). The RF model yielded similar results. Only 422/554 patients in the validation cohort provided a stool sample. Among these patients, the diagnostic accuracy of the protein signature (AUC=0.82) and faecal Calprotectin (AUC=0.86) did not differ (p=0.07). A complex protein signature may preclude its introduction into clinical practice. Simplified models were therefore constructed using discovery cohort data and stepwise forward logistic regression with hsCRP as the null model. When applied to the validation cohort, the diagnostic accuracy of the shortest signature, comprising hsCRP and interleukin (IL)-17A (AUC=0.80), was superior to hsCRP (Figure 3, AUC=0.69, p=1.7×10-8). Conclusion A serum protein signature of hsCRP and IL-17A was superior to hsCRP in differentiating treatment-naïve IBD from symptomatic controls. Taking into account that many patients do not accept to provide faecal samples, we believe this signature has the potential to become an accessible and scalable test that can shorten the diagnostic delay in IBD.

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets.

Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.

Variational mode decomposition for surface and intramuscular EMG signal denoising

Electromyographic signals contaminated with noise during the acquisition process affect the results of follow-up applications such as disease diagnosis, motion recognition, gesture recognition, and human–computer interaction. This paper proposes a denoising technique based on the variational mode decomposition (VMD) for both surface electromyography signals (sEMG) and intramuscular electromyography signals (iEMG). sEMG and iEMG obtained from 5 healthy subjects were first decomposed using VMD into respective variational mode functions (VMFs), then thresholds were set to remove the noise, and finally, the denoised signal was reconstructed. The denoising efficacy of interval thresholding (IT) and iterative interval thresholding (IIT) techniques in combination with SOFT, HARD, and smoothly clipped absolute deviation (SCAD) thresholding operators was quantitatively evaluated by using Signal to Noise Ratio (SNR) and further statistically validated by Friedman test. The results demonstrated that IIT provides better SNR values than IT at all noise levels (P-value < 0.05) for sEMG signals. For iEMG, IIT outperformed IT at 0db and 5db noise levels, but at a noise level of 10db and 15db, IT outperformed IIT. However, the results for the 10db noise level were statistically insignificant. The SOFT thresholding operator outperforms HARD and SCAD at all noise levels for sEMG, as well as iEMG (P-value < 0.05). The study demonstrates that the combination of the IIT thresholding technique with the VMD-based SOFT thresholding operator yields the best denoising results while retaining the original signal characteristics. The proposed method can be used in the fields of disease diagnosis, pattern recognition, and movement classification.

The impact of temporal lobe epilepsy surgery on picture naming and its relationship to network metric change

BackgroundAnterior temporal lobe resection (ATLR) is a successful treatment for medically-refractory temporal lobe epilepsy (TLE). In the language-dominant hemisphere, 30%- 50% of individuals experience a naming decline which can impact upon daily life. Measures of structural networks are associated with language performance pre-operatively. It is unclear if analysis of network measures may predict post-operative decline. MethodsWhite matter fibre tractography was performed on preoperative diffusion MRI of 44 left lateralised and left resection individuals with TLE to reconstruct the preoperative structural network. Resection masks, drawn on co-registered pre- and post-operative T1-weighted MRI scans, were used as exclusion regions on pre-operative tractography to estimate the post-operative network. Changes in graph theory metrics, cortical strength, betweenness centrality, and clustering coefficient were generated by comparing the estimated pre- and post-operative networks. These were thresholded based on the presence of the connection in each patient, ranging from 75% to 100% in steps of 5%. The average graph theory metric across thresholds was taken.We incorporated leave-one-out cross-validation with smoothly clipped absolute deviation (SCAD) least absolute shrinkage and selection operator (LASSO) feature selection and a support vector classifier to assess graph theory metrics on picture naming decline. Picture naming was assessed via the Graded Naming Test preoperatively and at 3 and 12 months post-operatively and the outcome was classified using the reliable change index (RCI) to identify clinically significant decline. The best feature combination and model was selected using the area under the curve (AUC). The sensitivity, specificity and F1-score were also reported. Permutation testing was performed to assess the machine learning model and selected regions difference significance. ResultsA combination of clinical and graph theory metrics were able to classify outcome of picture naming at 3 months with an AUC of 0.84. At 12 months, change in strength to cortical regions was best able to correctly classify outcome with an AUC of 0.86. Longitudinal analysis revealed that betweenness centrality was the best metric to identify patients who declined at 3 months, who will then continue to experience decline from 3 to 12 months. Both models were significantly higher AUC values than a random classifier. ConclusionOur results suggest that inferred changes of network integrity were able to correctly classify picture naming decline after ATLR. These measures may be used to prospectively to identify patients who are at risk of picture naming decline after surgery and could potentially be utilised to assist tailoring the resection in order to prevent this decline.

Sparse Estimation in Finite Mixture of Accelerated Failure Time and Mixture of Regression Models with R Package fmrs

Variable selection in large-dimensional data has been extensively studied in different settings over the past decades. In a recent article, Shokoohi et. al. [29, DOI:10.1214/18-AOAS1198] proposed a method for variable selection in finite mixture of accelerated failure time regression models for studies on time-to-event data to capture heterogeneity within the population and account for censoring. In this paper, we introduce the fmrs package, which implements the variable selection methodology for such models. Furthermore, as a byproduct, the fmrs package facilitates variable selection in finite mixture regression models. The package also incorporates a tuning parameter selection mechanism based on component-wise bic. Commonly used penalties, such as Least Absolute Shrinkage and Selection Operator, and Smoothly Clipped Absolute Deviation, are integrated into fmrs. Additionally, the package offers an option for non-mixture regression models. The C language is chosen to boost the optimization speed. We provide an overview of the fmrs principles and the strategies employed for optimization. Hands-on illustrations are presented to help users get acquainted with fmrs. Finally, we apply fmrs to a lung cancer dataset and observe that a two-component mixture model reveals a subgroup with a more aggressive form of the disease, displaying a lower survival time.

Ridge regression coupled with a new uninformative variable elimination algorithm as a new descriptor screening method: Application of data reduction in QSAR study of some sulfonated derivatives as c-Met inhibitors

An uninformative variable elimination algorithm was combined with the Ridge regression method. This combination makes the penalized Ridge method, which is essentially incapable of deleting or selecting a variable, an efficient descriptor screening method for screening of descriptors in QSAR studies. The importance of descriptors was used to identify uninformative and redundant descriptors. The descriptor importance was defined as the ratio of the mean coefficients to the standard deviation of the coefficients derived from the leave-one-out (LOO) Ridge models. Then, with an iterative algorithm, among 392 molecular descriptors obtained after initial preprocessing, 358 uninformative descriptors were identified and removed from the data set. The remaining 34 descriptors were used for further variable selection and modeling through the smoothly clipped absolute deviation (SCAD) method. The proposed screening method was used to screen and reduce the size of QSAR data containing 63 compounds as c-Met inhibitors with anti-cancer activity. For comparison, the SCAD models were constructed using original high-dimensional data (SCAD), and the reduced-size data via the UVE-Ridge screening method (UVE-Ridge-SCAD). Both models were used to predict biological activities (pIC50) for compounds in the external test set. The coefficients of determination (R2) and the FIT parameter for the test data predicted using the SCAD model (with 12 selected descriptors) were equal to 0.8042 and 3.78, respectively. The UVE-Ridge-SCAD model (with 9 descriptors) showed R2 and FIT values of 0.9025 and 4.38, respectively. Higher R2 and FIT values indicate the excellent predictive power of the UVE-Ridge-SCAD model. Finally, the UVE-Ridge-SCAD model was used to suggest new high-potency compounds. The accuracy of the proposed compounds was investigated using ligand-receptor interactions.