Set Of Features Research Articles

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using theOHDSI network.

Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. Model development included 41929 patients, 14.6% with POU. The external validation included 31932 (UK), 23100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

Feature Detection Based on Imaging and Genetic Data Using Multi-Kernel Support Vector Machine–Apriori Model

Alzheimer’s disease (AD) is a significant neurological disorder characterized by progressive cognitive decline and memory loss. One essential task is understanding the molecular mechanisms underlying brain disorders of AD. Detecting biomarkers that contribute significantly to the classification of AD is an effective means to accomplish this essential task. However, most machine learning methods used to detect AD biomarkers require lengthy training and are unable to rapidly and effectively detect AD biomarkers. To detect biomarkers for AD accurately and efficiently, we proposed a novel approach using the Multi-Kernel Support Vector Machine (SVM) with Apriori algorithm to mine strongly associated feature sets from functional magnetic resonance imaging (fMRI) and gene expression profiles. Firstly, we downloaded the imaging data and genetic data of 121 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and transformed gene sequences into labeled sequences by encoding the four types of bases (A, T, C, and G) into distinct labels. Subsequently, we extracted the first 130 temporal sequences of brain regions and employed Pearson correlation analysis to construct “brain region gene pairs”. The integration of these data allowed us to explore the correlations between genes and brain regions. To improve classification accuracy and feature selection, we applied the Apriori algorithm to the multi-kernel SVM, dynamically building feature combinations and continuously validating classification results. By iteratively generating frequent itemsets, we obtained important brain region gene pairs. Experimental results show the effectiveness of our proposed approach. The Multi-Kernel SVM with Apriori model achieves an accuracy of 92.9%, precision of 95%, and an F1 score of 95% in classifying brain region-gene pairs within the AD–Late mild cognitive impairment (AD-LMCI) group. The amygdala, BIN1, RPN2, and IL15 associated with AD have been identified and demonstrate potential in identifying potential pathogenic factors of AD. The selected brain regions and associated genes may serve as valuable biomarkers for early AD diagnosis and better understanding of the disease’s molecular mechanisms. The integration of fMRI and gene data using the Multi-Kernel SVM–Apriori model holds great potential for advancing our knowledge of brain function and the genetic basis of neurological disorders. This approach provides a valuable tool for neuroscientists and researchers in the field of genomics and brain imaging studies.

Mitigating the curse of dimensionality using feature projection techniques on electroencephalography datasets: an empirical review

Electroencephalography (EEG) is commonly employed to diagnose and monitor brain disorders, however, manual analysis is time-consuming. Hence, researchers nowadays are increasingly leveraging artificial intelligence (AI) techniques for automatic analysis of EEG, involving task-specific feature extraction and classification. Extracting multiple non-linear features from multiple EEG channels enhances the performance of classification, but it also results in high-dimensional features. However, the presence of the "curse of dimensionality" poses challenges for classifiers in AI-based EEG analyzers, leading to overfitting and complexity in classification. Therefore, in this empirical review, the effectiveness of the existing dimensionality reduction techniques to mitigate the curse of dimensionality in EEG feature sets is analyzed. We begin this study, by overviewing the extracted high-dimensional features from EEG signals related to disorders such as schizophrenia, alcoholism, focal seizures, focal seizures with deep features, and depression. Subsequently, 23 reduction techniques were reviewed, which transform the high-dimensional EEG features into a new reduced feature space to improve the classification. The impact of the reduced features was evaluated using traditional AI-based classifiers (support vector machine (SVM) and k-nearest neighbor (k-NN)). 10-fold cross-validation was performed for training and testing, and the performance was evaluated using accuracy, selected features, and dimensionality reduction rate (DRR) metrics. Comprehensive analysis of projection techniques on diverse EEG datasets offers valuable insights to assist researchers in selecting the most suitable technique. The hybrid projection technique termed principal component analysis-based t-distributed stochastic neighbor embedding (PCA+t-SNE) achieved an impressive average accuracy of 93.36%, surpassing the k-NN classifier without reduction techniques.

Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram

Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.

Слабо-контролируемая групповая классификация

В работе решается задача слабо-контролируемого обучения в постановке групповой бинарной классификации. Предполагается, что каждый объект выборки может включать набор подобъектов, относящихся к разным классам. Предлагаемый метод решения основан на выборе информативного признакового пространства и фильтрации обучающей выборки. Описывается применение разработанного метода для прогнозирования степени поражения участков головного мозга у пациентов с ишемическим инсультом по снимкам компьютерной томографии. Weakly supervised learning implies possible uncertainty or fuzziness of the labelling. Current study addresses this problem using the formulation of group binary classification. It is assumed that each sample object may include a set of sub-objects belonging to one of two classes. Objects are described by a set of features; the predicted feature determines the degree to which an object belongs to the “positive” class. It is required to construct a decision function from the training sample to predict the target feature for new objects. The proposed method is based on the selection of informative feature space and filtering the training sample. Both the selection of informative features and the removal of noise observations are carried out on the basis of analysis of the local environment of objects. The degree of similarity between the object and the class is determined by the k nearest neighbours of the object, taking into account their degree of belonging to the target class. For an experimental study of the developed method, the real problem of analyzing tomography images of the brain to predict the degree of damage to its areas in stroke is solved. The results are compared with a number of known methods. A method for constructing a decision function for predicting the degree of belonging of an object to the target class has been developed. The results of an experimental study and comparison with a number of well-known machine learning algorithms (random forest, support vector machine, kNN) confirmed the efficiency of the method for solving the problem of predicting the degree of damage to brain areas in stroke patients. Unlike other similar algorithms, the proposed method allows establishing a set of the most informative features in order to improve the interpretability of the solution and reduce the effect of overfitting.

AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban Scenes

In urban settings, roadside infrastructure LiDAR is a ground-based remote sensing system that collects 3D sparse point clouds for the traffic object detection of vehicles, pedestrians, and cyclists. Current anchor-free algorithms for 3D point cloud object detection based on roadside infrastructure face challenges related to inadequate feature extraction, disregard for spatial information in large 3D scenes, and inaccurate object detection. In this study, we propose AFRNet, a two-stage anchor-free detection network, to address the aforementioned challenges. We propose a 3D feature extraction backbone based on the large sparse kernel convolution (LSKC) feature set abstraction module, and incorporate the CBAM attention mechanism to enhance the large scene feature extraction capability and the representation of the point cloud features, enabling the network to prioritize the object of interest. After completing the first stage of center-based prediction, we propose a refinement method based on attentional feature fusion, where fused features incorporating raw point cloud features, voxel features, BEV features, and key point features are used for the second stage of refinement to complete the detection of 3D objects. To evaluate the performance of our detection algorithms, we conducted experiments using roadside LiDAR data from the urban traffic dataset DAIR-V2X, based on the Beijing High-Level Automated Driving Demonstration Area. The experimental results show that AFRNet has an average of 5.27 percent higher detection accuracy than CenterPoint for traffic objects. Comparative tests further confirm that our method achieves high accuracy in roadside LiDAR object detection.

A nanosecond-scale CuI synaptic memristor prepared by a solution-based process

Owing to the synaptic behaviors and functions, memristors are intensively studied as a critical component for the neuromorphic computing system which is considered as an effective scenario to tackle the performance bottleneck existing in modern computers based on the von Neumann architecture. A novel synaptic device base on the CuI memristor prepared with a solution-based process is proposed in this work, and a set of synaptic features are emulated. The electrochemical metallization contributes to the resistive switching behavior and small operating voltages (Vset = 0.67 V and Vreset = −0.33 V). Moreover, the CuI memristor exhibits the small switching energy (23 pJ) and ultraquick switching speed (100 ns). A large CuI memristor array is realized experimentally, by which the hardware-based multiply-and-accumulate (MAC) operation is implemented. Furthermore, the hardware-based convolution computation in image processing is realized based on the MAC operation, demonstrating the potential applications in image processing requiring the high degree of parallelism.

Radiomics analysis of patellofemoral joint improves knee replacement risk prediction: Data from the Multicenter Osteoarthritis Study (MOST)

ObjectiveKnee replacement (KR) is the last-resort treatment for knee osteoarthritis. Although radiographic evidence of tibiofemoral joint has been widely adopted for prognostication, patellofemoral joint has gained little attention and may hold additional value for further improvements. We aimed to quantitatively analyse patellofemoral joint through radiomics analysis of lateral view radiographs for improved KR risk prediction. DesignFrom the Multicenter Osteoarthritis Study dataset, we retrospectively retrieved the initial-visit lateral left knee radiographs of 2943 patients aged 50 to 79. They were split into training and test cohorts at a 2:1 ratio. A comprehensive set of radiomic features were extracted within the best-performing subregion of patellofemoral joint and combined into a radiomics score (RadScore). A KR risk score, derived from Kellgren-Lawrence grade (KLG) of tibiofemoral joint and RadScore of patellofemoral joint, was developed by multivariate Cox regression and assessed using time-dependent area under receiver operating characteristic curve (AUC). ResultsWhile patellofemoral osteoarthritis (PFOA) was insignificant during multivariate analysis, RadScore was identified as an independent risk factor (multivariate Cox p-value < 0.001) for KR. The subgroup analysis revealed that RadScore was particularly effective in predicting rapid progressor (KR occurrence before 30 months) among early- (KLG < 2) and mid-stage (KLG ​= ​2) patients. Combining two joints radiographic information, the AUC reached 0.89/0.87 for predicting 60-month KR occurrence. ConclusionsThe RadScore of the patellofemoral joint on lateral radiographs emerges as an independent prognostic factor for improving KR prognosis prediction. The KR risk score could be instrumental in managing progressive knee osteoarthritis interventions.

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

Speech emotion recognition via graph-based representations

Speech emotion recognition (SER) has gained an increased interest during the last decades as part of enriched affective computing. As a consequence, a variety of engineering approaches have been developed addressing the challenge of the SER problem, exploiting different features, learning algorithms, and datasets. In this paper, we propose the application of the graph theory for classifying emotionally-colored speech signals. Graph theory provides tools for extracting statistical as well as structural information from any time series. We propose to use the mentioned information as a novel feature set. Furthermore, we suggest setting a unique feature-based identity for each emotion belonging to each speaker. The emotion classification is performed by a Random Forest classifier in a Leave-One-Speaker-Out Cross Validation (LOSO-CV) scheme. The proposed method is compared with two state-of-the-art approaches involving well known hand-crafted features as well as deep learning architectures operating on mel-spectrograms. Experimental results on three datasets, EMODB (German, acted) and AESDD (Greek, acted), and DEMoS (Italian, in-the-wild), reveal that our proposed method outperforms the comparative methods in these datasets. Specifically, we observe an average UAR increase of almost 18%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$18\\%$$\\end{document}, 8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\\%$$\\end{document} and 13%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$13\\%$$\\end{document}, respectively.

Clarifying the relationship between mental illness and recidivism using machine learning: A retrospective study.

There is currently inconclusive evidence regarding the relationship between recidivism and mental illness. This retrospective study aimed to use rigorous machine learning methods to understand the unique predictive utility of mental illness for recidivism in a general population (i.e.; not only those with mental illness) prison sample in the United States. Participants were adult men (n = 322) and women (n = 72) who were recruited from three prisons in the Midwest region of the United States. Three model comparisons using Bayesian correlated t-tests were conducted to understand the incremental predictive utility of mental illness, substance use, and crime and demographic variables for recidivism prediction. Three classification statistical algorithms were considered while evaluating model configurations for the t-tests: elastic net logistic regression (GLMnet), k-nearest neighbors (KNN), and random forests (RF). Rates of substance use disorders were particularly high in our sample (86.29%). Mental illness variables and substance use variables did not add predictive utility for recidivism prediction over and above crime and demographic variables. Exploratory analyses comparing the crime and demographic, substance use, and mental illness feature sets to null models found that only the crime and demographics model had an increased likelihood of improving recidivism prediction accuracy. Despite not finding a direct relationship between mental illness and recidivism, treatment of mental illness in incarcerated populations is still essential due to the high rates of mental illnesses, the legal imperative, the possibility of decreasing institutional disciplinary burden, the opportunity to increase the effectiveness of rehabilitation programs in prison, and the potential to improve meaningful outcomes beyond recidivism following release.

Bi-stage feature selection for crop mapping using grey wolf metaheuristic optimization

Machine learning classifiers have been widely used for crop mapping; however, employing numerous features can reduce classifier accuracy. Feature selection is a widely recognized technique that addresses the issue of high dimensionality by choosing the most relevant subset of features with the highest degree of relevance and practicality. Therefore, the optimal combination of feature selection methods and classifiers for high-precision crop mapping remains an open problem. In this study, we introduce a novel hybrid feature selection approach to obtain the optimal subset of features for effective crop mapping. Initially, spectro-temporal remote sensing features from the dataset were ranked using two distinct feature selection techniques: Mutual Information (MI) and ReliefF filter. The most relevant features from each filter-based approach were combined into a union subset. Subsequently, the Gray Wolf Optimization (GWO), a metaheuristic optimization technique, was applied to enhance the feature set generated in the initial step. In the final stage, a random forest classifier leveraged the optimized feature subset for accurate crop type prediction. The effectiveness of the proposed approach was evaluated in Behiera governorate, Egypt. Comparative analysis against existing crop mapping methods demonstrated the superior performance of the proposed approach, achieving an accuracy rate of 82%. Furthermore, this approach showcased a significant reduction in the feature count, streamlining the set from 308 down to only 26 features.

Battery Remaining Useful Life Prediction Using Machine Learning Models: A Comparative Study

Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks.

Gearbox faults severity classification using Poincaré plots of acoustic emission signals

Classification of fault severity in gearboxes using Acoustic Emission (AE) signals is challenging because such signals represent a highly non-linear and possibly chaotic system. Due to the common assumption of linearity, the statistical features extracted from these systems are suboptimal for the classification of fault severity. Hence, this paper uses the Poincaré plot (PP) of Acoustic Emission (AE) signals to extract useful features to classify fault type and severity in gearboxes. For this development, four fault types were applied over different gears and then tested on an experimental condition monitoring bench: broken tooth, pitting, scuffing, and cracks, each with nine severity levels. Then, the feature set was extracted from the conventional 2-D PP, composed of shape-related features and a set of features known as complex correlation measurements (CCM). The fault type and severity classification was performed using four frequency bands. Low and band-pass filtered signals obtained the highest classification accuracy with Random Forest (RF): the fault type was classified with an accuracy of 99.69%, the severity was classified with an accuracy depending on the fault type corresponding to pitting 98.76%, cracks 98.71%, broken tooth 98.96%, and scuffing 98.51%. The PP features set has a low computational cost even for large datasets representing AE signals, which can benefit the practical possibility of the implementation with high classification accuracy for different types of fault and severity levels in a gearbox.

Comparative analysis of ROCKET-driven and classic EEG features in predicting attachment styles

Predicting attachment styles using AI algorithms remains relatively unexplored in scientific literature. This study addresses this gap by employing EEG data to evaluate the effectiveness of ROCKET-driven features versus classic features, both analyzed using the XGBoost machine learning algorithm, for classifying ‘secure’ or ‘insecure’ attachment styles.Participants, fourth-year engineering students aged 20–35, first completed the ECR-R questionnaire. A subset then underwent EEG sessions while performing the Arrow Flanker Task, receiving success or failure feedback for each trial.Our findings reveal the effectiveness of both feature sets. The dataset with ROCKET-derived features demonstrated an 88.41% True Positive Rate (TPR) in classifying ‘insecure’ attachment styles, compared to the classic features dataset, which achieved a notable TPR as well. Visual representations further support ROCKET-derived features’ proficiency in identifying insecure attachment tendencies, while the classic features exhibited limitations in classification accuracy. Although the ROCKET-derived features exhibited higher TPR, the classic features also presented a substantial predictive ability.In conclusion, this study advances the integration of AI in psychological assessments, emphasizing the significance of feature selection for specific datasets and applications. While both feature sets effectively classified EEG-based attachment styles, the ROCKET-derived features demonstrated a superior performance across multiple metrics, making them the preferred choice for this study.

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images.

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease's influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue's nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework's higher accuracy and precision rate.

Unsupervised damage assessment under varying ambient temperature based on an adjusted artificial neural network and new multivariate covariance-based distances

Temperature variability is one of the critical environmental conditions that causes confusing changes in structural properties and dynamic responses of bridges similar to damage. In this case, false alarms and mis-detection are among the major errors in health monitoring of such civil structures. High damage detectability is another significant challenge in bridge health monitoring. To deal with these issues, this article proposes an unsupervised damage assessment technique comprising two steps of data normalization and novelty detection. For the first step, an adjusted artificial neural network is considered to remove the effects of temperature variability from dynamic features (modal frequencies). This process is carried out by an auto-associative neural network by adjusting its hidden layer neurons through a new hyperparameter selection algorithm. Using normalized features obtained from the first step, this article proposes three multivariate covariance-based distances called linear dissimilarity analysis, multivariate Kullback–Leibler divergence, and multivariate Bregman distance to compute damage indices or novelty scores for damage assessment. The fundamental principles of these distances lie in three aspects: dividing the normalized features into segments, estimating the covariances of segmented feature sets, and incorporating the estimated covariances into the proposed distance measures. The major contributions of this article include proposing three non-parametric distance measures and developing an unsupervised data normalization framework via a new hyperparameter tuning algorithm for adjusting an artificial neural network. A concrete box-girder bridge is considered to verify the proposed approach, along with several comparative studies. Results show that the method presented here can mitigate severe temperature variability and increase damage detectability with superiority over some traditional and state-of-the-art damage assessment techniques.

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation.

Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort. Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites. Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model. The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.

A data driven approach to assess the petrophysical parametric sensitivity for lithology identification based on ensemble learning

Lithology identification is essential for geological analysis and reservoir characterization of hydrocarbon bearing formation. Conventional approaches of lithology identification are not only inconvenient but also time-consuming and expensive. Machine learning (ML) algorithms have been introduced as an effective, reliable, and accurate method of lithology identification using well-log data due to their ability to recognize the nonlinear relationship between log responses and lithology. In this study, we employed the XGBoost classifier model to assess the parametric sensitivity of the logging features in identifying lithology. Sequential Forward Feature Selection (SFFS) approach was applied to determine the most significant set of logging features that yields maximum classification accuracy. Nine logging parameters were used as the input of the XGBoost model to classify five lithology classes. Hyper-parameters were tuned within appropriate tuning search range to optimize the model performance along with Grid Search and 5-fold cross validation. The training and testing accuracy obtained from the model are 96% and 90.97%, respectively. The optimized XGBoost model showed the lowest precision, recall, and F1 score for the clay class. The shale class, on the other side, had the highest precision, recall, and F1 score of 95%, 94%, and 94%, respectively. The best set of logging features for identifying formation lithology is determined to be [PEF, SP, LLD, CALI, MSFL, NPHI, LLS], with a classification accuracy of 92.10% for the testing dataset. The least significant logging parameters for identifying lithology were found to be DT and GR, as their addition with the best input feature set, resulted in a negative deviation in classification accuracy. Finally, based on the classification accuracy, we ranked the top three sets of logging features, those contributed significantly.

Plains Cree Order as alternation

Abstract This paper describes the Plains Cree phenomenon of Order as a form of alternation not yet described as such in the literature. First, we provide a brief description of relevant Plains Cree grammar and Order as a phenomenon. This is followed by an overview of how the concept of alternation has been used in linguistics as an analytic tool. Finally, we discuss how conceiving of Order as an alternation allows for not only a better understanding of the phenomenon, but also results in a new type of alternation. We name this type of alternation a paradigmatic alternation and define it as any alternation where some lexeme can make use of two or more alternative paradigms of the same size and shape but with different morphological exponents, while representing the same set of grammatical features. We exemplify this with Plains Cree, where subject/object agreement has two distinct paradigms which verbs can take while retaining similar meanings. A brief comparison with of a similar phenomenon, Koiari tense/aspect, is also included.