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Results for “Feature selection”

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PubMed1986

Identification of best electrocardiographic leads for diagnosing anterior and inferior myocardial infarction by statistical analysis of body surface potential maps.

In view of the increasing interest in quantifying and modifying the size of myocardial infarction (MI), it is important to look for clinically practical subsets of electrocardiographic leads that allow the earliest and most accurate diagnosis of the presence and electrocardiographic type of MI. A practical approach is described, taking advantage of the increased information content of body surface potential maps over standard electrocardiographic techniques for facilitating clinical use of body surface potential maps for such a purpose. Multivariate analysis was performed on 120-lead electrocardiographic data, simultaneously recorded in 236 normal subjects, 114 patients with anterior MI and 144 patients with inferior MI, using as features instantaneous voltages on time-normalized QRS and ST-T waveforms. Leads and features for optimal separation of normal subjects from, respectively, anterior MI and inferior MI patients were selected. Features measured on leads originating from the upper left precordial area, lower midthoracic region and the back correctly identified 97% of anterior MI patients, with a specificity of 95%; in patients with inferior MI, features obtained from leads located in the lower left back, left leg, right subclavicular area, upper dorsal region and lower right chest correctly classified 94% of the group, with specificity kept at 95%. Most features were measured in early and mid-QRS, although very potent discriminators were found in the late portion of the T wave.(ABSTRACT TRUNCATED AT 250 WORDS)

Action Potentials↗

PubMed1975

Development of multiple dimension use in form classification.

The use of multiple form dimensions in pattern classification was studied with adults and children in grades 2 and 5. Each subject sorted 30 8-sided random polygons first into 2, then into 3, and finally into 4 groups and repeated the procedure 1 week later. A series of discriminant analyses, using 9 physical form characteristics as predictors, was used to answer several developmental questions. Reliability of classification, number and saliency of features selected, and accuracy with which they were used all implied continuous development of perceptual skills. Multiple feature use in classification was evidenced at all age levels.

Achievement↗

PubMed2026

Essence: A benchmarking-validated transformer framework for early diagnosis of Parkinson's disease using cerebrospinal fluid protein biomarkers.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. The lack of objective molecular biomarkers limits early diagnosis and personalized treatment. Here, we propose Essence, a benchmarking-validated framework integrating cerebrospinal fluid (CSF) proteomics with traditional and deep learning models to identify robust protein signatures for PD. Using data from two independent cohorts, 1266 high-confidence proteins are quantified, among which 178 exhibit differential abundance between PD and healthy controls (HC). Through systematic benchmarking of ten machine learning algorithms and four neural architectures, the Transformer model consistently outperforms alternatives across multiple feature selection strategies, achieving an area under the receiver operating characteristic curve (AUC) of 1.0000 with only 35 features. Functional analyses of the top-ranked 35 proteins reveal enrichment in neuroinflammatory, synaptic, and oxidative stress-related pathways. Importantly, spatial transcriptomic profiling based on the Allen Brain Atlas shows region-specific expression of these biomarkers in PD-relevant brain structures, including the striatum, subthalamic nucleus, hippocampus, and white matter tracts. This anatomical alignment supports the functional relevance of the identified markers and highlights their potential utility in early-stage diagnosis and mechanistic understanding of PD.

Benchmarking↗

PubMedDate not supplied

Fine needle aspiration biopsy diagnosis of mucoepidermoid carcinoma. Statistical analysis.

Fine needle aspiration (FNA) biopsy is an increasingly popular method for the evaluation of salivary gland tumors. Of the common salivary gland tumors, mucoepidermoid carcinoma is probably the most difficult to diagnose accurately by this means. A series of 96 FNA biopsy specimens of salivary gland masses, including 34 mucoepidermoid carcinomas, 51 other benign and malignant neoplasms, 7 nonneoplastic lesions and 4 normal salivary glands, were analyzed in order to identify the most useful criteria for diagnosing mucoepidermoid carcinoma. Thirteen cytologic criteria were evaluated in the FNA specimens, and a stepwise logistic regression analysis was performed. The three cytologic features selected as most predictive of mucoepidermoid carcinoma were intermediate cells, squamous cells and overlapping epithelial groups. Using these three features together, the sensitivity and specificity of accurately diagnosing mucoepidermoid carcinoma were 97% and 100%, respectively.

Biopsy, Needle↗

PubMed2026

BaGGLS: a Bayesian shrinkage framework for interpretable modeling of interactions in high-dimensional biological data.

MOTIVATION: Biological data is often high dimensional, noisy, and governed by complex interactions among sparse signals. This poses major challenges for interpretability and reliable feature selection. Tasks such as identifying motif interactions in genomics exemplify these difficulties, as only a small subset of biologically relevant features (e.g. motifs) are typically active, and their effects are often non-linear and context-dependent. While statistical approaches often result in more interpretable models, deep learning models have proven effective in modeling complex interactions and prediction accuracy, yet their black-box nature limits interpretability. RESULTS: We introduce BaGGLS, a flexible and interpretable probabilistic binary regression model designed for high-dimensional biological inference involving feature interactions. BaGGLS incorporates a Bayesian group global-local shrinkage prior, aligned with the group structure introduced by interaction terms. This prior encourages sparsity while retaining interpretability, helping to isolate meaningful signals and suppress noise. To enable scalable inference, we employ a partially factorized variational approximation that captures posterior skewness and supports efficient learning even in large feature spaces. In extensive simulations, we compare BaGGLS to frequentist probit regressions (unconstrained and with L1-penalty) as well as a probit model with Markov Chain Monte Carlo (MCMC) sampling under a horseshoe prior. We can show that BaGGLS outperforms the other methods with regard to interaction detection and is many times faster than MCMC sampling under the horseshoe prior. We also demonstrate the usefulness of BaGGLS in the context of interaction discovery from motif scanner outputs (e.g. Find Individual Motif Occurrences (FIMO)) and noisy attribution scores from deep learning models. This shows that BaGGLS is a promising approach for uncovering biologically relevant interaction patterns, with potential applicability across a range of high-dimensional tasks in computational biology. AVAILABILITY: Code is available at gitlab.com/dacs-hpi/baggls.

Bayes Theorem↗

PubMed1990

Molecular conformational space analysis using computer graphics: going beyond FRODO.

The molecular graphics program FRODO has been modified to support analytical animation of molecular dynamics trajectories. The enhanced program, mdFRODO, supports all features available in FRODO and is interfaced to GROMOS. A variety of analytical animation modes is included. Extensive coloring and atom selection features are implemented to aid the user in distinguishing features of interest in a set of conformations. Molecular conformational space can be analyzed efficiently and comprehended. Animations may be viewed in stereo, and the animated object can be overlaid with any of the standard FRODO objects. The mdFRODO program is of wide use in molecular dynamics, X-ray crystallography and two-dimensional NMR work. Examples illustrating various aspects of collective motion in protein molecules are given and discussed.

Computer Graphics↗

PubMed1991

The development of perceived structure and attention: evidence from divided and selective attention tasks.

Three experiments provide converging evidence for the view that both perceived structure and attention change during the elementary school years. Kindergarteners, second graders, and adults performed three speeded tasks: divided attention to conjunctions of features, selective attention to orthogonal dimensions and selective attention to correlated dimensions. The tasks were performed with sizes and shapes that were either spatially integrated or spatially separated. In the divided attention task, conjunctions were identified as quickly as single features with integrated stimuli at all ages, but conjunctions were identified more slowly than single features with separated stimuli by all age group. In the orthogonal dimensions task, interference was observed with integrated stimuli across ages, but the interference in adult performance was asymmetric. With separated stimuli, interference was gradually eliminated with increasing age. In correlated dimensions tasks, younger children showed a redundancy gain with integrated stimuli, but no gain was observed in the performances of the older subjects. With separated stimuli there was no redundancy gain at any age. These results were interpreted to mean that integrated stimuli are initially perceived as wholes by all subjects, but that features become more accessible with increasing age. Even so, attention remains constrained by stimulus structure. In contrast, separated stimuli are initially perceived as features at all ages, and the improvement in performance with increasing age is attributable to the increasing command of attentional resources that accompanies development. Our discussion of these findings focuses on three issues: multiple trends in perceptual development, the characteristics of an adequate theory of perceptual representation and processing, and a comparison of the separability hypothesis and other developmental accounts of perceptual development.

Adult↗

PubMed2026

Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.

Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.

Prostatic Neoplasms↗

PubMed2026

Linking MRI radiomics to transcriptomics-based radiosensitivity in lower-grade glioma: A radiogenomic framework.

BACKGROUND: RSI is a transcriptomics-based biomarker associated with radiotherapy outcomes, but its clinical application is constrained by the requirement for tumor tissue and RNA sequencing. This study investigates whether MRI-derived radiomic features can reflect RSI-defined intrinsic radiosensitivity in lower-grade glioma.This addresses a critical gap arising from the limited availability of matched imaging and genomic data in routine clinical practice. METHODS: MRI-derived radiomic features were extracted from FLAIR images of lower-grade glioma patients obtained from TCIA and matched with transcriptomic data from TCGA. A total of 107 patients with both MRI and RNA sequencing data were included in the radiogenomic analysis. Radiomic features were ranked using a Borda-based ensemble feature selection strategy. Five supervised machine-learning classifiers were trained to predict RSI-based radiosensitivity classification, and model interpretability was assessed using SHAP within radiogenomic framework. RESULTS: Classification performance increased with feature number and stabilized at compact subset of 13 radiomic features. Logistic regression showed stable performance with an AUC of 0.82 (95 % CI: 0.71-0.93). SHAP analysis indicated that heterogeneity-related texture features were dominant contributors to model predictions, with many associated with the RR phenotype, while others were linked to the RS phenotype. CONCLUSION: An MRI-based radiomic signature enables non-invasive prediction of RSI-defined radiosensitivity in lower-grade glioma. Rather than offering an immediately deployable clinical tool, this study establishes a proof-of-concept radiogenomic framework demonstrating that intrinsic radiosensitivity, traditionally assessed through invasive molecular assays, can be approximated using quantitative imaging features. These findings highlight the potential of imaging-based radiosensitivity assessment and provide a foundation for future radiogenomic investigations.

Lower-grade glioma↗

PubMed1990

Interaction of porphyrin-containing macrotetracyclic receptor molecule with single-stranded and double-stranded polynucleotides. A photophysical study.

Photophysical methods have been used to study the interaction with nucleic acids of a macrotetracyclic cryptand molecule, Pbiph, containing a porphyrin groups, two macrocycles, and a biphenyl bridge. Pbiph binds with a higher affinity to single-stranded polynucleotides than to double-stranded ones. This selectivity, observed by binding and competition studies, using absorption and fluorescence spectroscopy, is pH dependent. Pbiph does not intercalate into double helices and is suggested to bind into the major groove. These features, selective single-strand binding and nonintercalation, are attributed to steric effects of the bulky Pbiph molecule, resulting from the macropolyclic cryptand cage structure.

DNA↗

PubMed1977

Development of psychomotor objectives for classroom of clinical education in physical therapy.

A systematic method for developing and evaluating psychomotor objectives for students in clinical and laboratory education was presented. In preparing objectives for motor learning, the instructor must perform a task analysis of each skill to be taught. The analysis involves 1) identifying components of the movement, 2) salient features of facilitating stimuli, such as placement of grips, intensity and speed of stimulation, and 3) timing of commands or resistance. The senses, such as vision or kinesthesia, through which the student is to perceive the selected features must be chosen. After determining the level of skill to be attained, teaching methods are easily fit to the objectives. A simple checklist aids evaluation of student competency and provides immediate feedback on performance level.

Humans↗

PubMed1986

Classification of lung carcinoma by means of digital nuclear image analysis.

An investigation was performed of the maximum discriminating efficiency for each subgroup of digital nuclear image features and of the overall classification of nuclei from three types of human lung carcinomas in histologic sections: adenocarcinoma, small-cell carcinoma and squamous-cell carcinoma. The results indicate that, for each subgroup of features, the nuclei of the small-cell carcinomas are generally "correctly" classified in a higher percentage (80% to 100%) than are the nuclei of the adenocarcinomas (46% to 74%) and squamous-cell carcinomas (29% to 68%). The discriminant analysis for the overall classification selected features from most of the subgroups, suggesting that it is useful to perform nuclear image analysis with many subgroups having different properties. The overall classifications for the nuclei of the adenocarcinomas, small-cell carcinomas and squamous-cell carcinomas were, respectively, 81.4%, 93.2% and 74.7%. Before this technique can be applied to histopathologic diagnosis, a larger number of unselected lung carcinomas must be evaluated.

Adenocarcinoma↗

PubMed1977

Microbial carbohydrate specific antibodies distinguish between different stages of differentiating mouse cerebellum.

High titered anticarbohydrate antibodies were used to identify cell surface carbohydrates during different stages in histogenesis of mouse cerebellum in a micro tissue-culture system which mimics selected features of in vivo cerebellum development. Blockage of fiber formation within the first few days in vitro and inhibition of cell migrations by carbohydrate-specific antibodies served as an assay system for possible contributions of surface carbohydrates to the behavior of developing cerebellar cells. Microbial strains were selected on the basis of carbohydrate structures of their cell wall antigens, and anticarbohydrate antibodies were raised against treated whole bacteria and yeast in rabbits. We found that antibodies to mannan were active at all stages of development tested (embryonic day 13, E13; the day of birth, PO; and postnatal day 7, P7). Antibodies to sialic acids prepared against strains B and C of Neisseria meningitidis distinguish different subterminal structures: anti-B reacted with E13 and PO cerebellar cells, and anti-C mostly with cells older than P7. Antifetuin antibody recognized E13 and PO but not P7 cell populations. Pneumococcus C strain R36A-specific antibodies were effective only after coating cells to C type carbohydrate before application of the antibody. The results demonstrate that antimicrobiol carbohydrate antibodies cross-react with mammalian cell surface carbohydrate structures and therefore can be used as a powerful tool in tissue culture to analyse those structures which might control cell behaviors pertinent to cerebellar development.

Animals↗

PubMed2025

Opportunities for machine learning to predict cross-neutralization in FMDV serotype O.

Accurately estimating cross-neutralization between serotype O foot-and-mouth disease viruses (FMDVs) is critical for guiding vaccine selection and disease management. In this study, we developed a machine learning approach to estimate r1 values-an established measure of antigenic similarity-using VP1 sequence data and published virus neutralization titer (VNT) results. Our dataset comprised 108 serum-virus pairs representing 73 distinct FMDV strains. We applied Boruta feature selection and random forest classifiers, optimizing model performance through tenfold cross-validation and sub-sampling to address class imbalance. Predictors included pairwise amino acid distances, site-specific polymorphisms, and differences in potential N-glycosylation sites. Using a 0.3 r1 threshold to define cross-neutralization, the final model achieved high accuracy (0.96), sensitivity (0.93), and specificity (0.96) in training, and performed robustly on independent test sets - accuracy was 0.75 (95% CI 0.60 and 0.90), F1 score 0.86% and PPV 0.77. Importantly, key VP1 residues-positions 48, 100, 135, 150, and 151-emerged as strong predictors of antigenic relationships. Our results demonstrate the utility of integrating routinely generated genomic data with machine learning to inform vaccine candidate selection and anticipate immune interactions among circulating FMDV strains. This approach offers a practical tool for accelerating vaccine decision-making and can be adapted to other FMDV serotypes. The latest version of the r1 predictive model is available for access via a Shiny dashboard (https://dmakau.shinyapps.io/PredImmune-FMD/).

Foot-and-Mouth Disease Virus↗

PubMed2025

Predicting telomerase reverse transcriptase promoter mutation status in glioblastoma by whole-tumor multi-sequence magnetic resonance texture analysis.

OBJECTIVE: This study aimed to determine the feasibility of preoperative multi-sequence magnetic resonance texture analysis (MRTA) for predicting TERT promoter mutation status in IDH-wildtype glioblastoma (IDHwt GB). METHODS: The clinical and imaging data of 111 patients with IDHwt GB at our hospital between November 2018 and June 2023 were retrospectively analyzed as the training set, and those of 23 patients with IDHwt GB between July 2023 and November 2023 were interpreted as the validation set. We used molecular sequencing results to classify the training set into TERT promoter mutation and wildtype groups. Textural features of the whole-tumor volume were extracted, including T2-weighted imaging (T2WI), T2-fluid-attenuated inversion recovery, apparent diffusion coefficient (ADC) map, and contrast-enhanced T1-weighted imaging (CE-T1). All textural features were obtained using open-source pyradiomics. After feature selection, logistic regression was used to build prediction models, and a nomogram was generated. Finally, the model was validated using validation cohort. RESULTS: The CE-T1_Model (AUC 0.704) had a better predictive ability than the T2_Model (AUC 0.684) and ADC_Model (AUC 0.624). The MRI_Combined_Model (CE-T1, T2, and ADC texture features) (AUC 0.780) had a better predictive ability than the Clinical_Model (AUC 0.758). The Combined_Model (CE-T1, T2, ADC texture features, and clinical features) had the best predictive performance (AUC 0.871), with a sensitivity, specificity, and accuracy of 82.60 %, 83.30 %, and 80.18 %, respectively. The AUC, sensitivity, specificity, and accuracy in the validation cohort were 0.775, 86.70 %, 75.00 %, and 69.57 %, respectively. CONCLUSIONS: Whole-tumor multi-sequence MRTA can be used as non-invasive quantitative parameters to assist in the preoperative clinical prediction of TERT promoter mutation status in IDHwt GB.

Humans↗

PubMed1987

Event-related potentials: a critical review of methods for single-trial detection.

The analysis of ERP data has followed several lines over the last 20 years. The most prevalent method is simply to average ERPs for a given class of stimuli. The ERPs are compared for differences across classes of stimuli. Little other special data processing is used. The ERP comparisons are usually performed using visual examination of the wave-shapes. Sometimes statistics are calculated such as means, variances, and confidence limits. Linear filtering is used to reduce interference. Another approach is to model or analyze the ERP as a sequence of vectors or frames of data samples. These samples may be of the ERP time waveform or they may be of the frequency transform of the ERP waveform. The frames of data vary in length from the entire ERP waveform (500 to 1000 msec) to frames as short as ten sample points (100 msec). Recognition of an event in the ERP is achieved by computing a distance measure between parameter vectors for one class of stimuli and corresponding parameter vectors for another class of stimuli. Recognition is achieved by selecting the ERP with the lowest distance score. This approach is "pattern matching" and relies on two assumptions: adjacent frames of data are uncorrelated, and the variability of the data can be accounted for by the distance measured for all stimuli in the classes presented. Subject variability is generally not accounted for, other than to assume it is the same for all classes of stimuli. The data are clustered into a variety of reference patterns that represent particular manifestations of a particular stimulus. Another approach is "feature-based" recognition. The idea is to identify and automatically extract features of the data that can provide a characterization of stimuli. The features selected may be abstract. They are calculated from the data or transforms of the data.

Biometry↗

PubMed2025

CoxKAN: Kolmogorov-Arnold networks for interpretable, high-performance survival analysis.

MOTIVATION: Survival analysis is a branch of statistics that is crucial in medicine for modeling the time to critical events such as death or relapse, in order to improve treatment strategies and patient outcomes. Selecting survival models often involves a trade-off between performance and interpretability; deep learning models offer high performance but lack the transparency of more traditional approaches. This poses a significant issue in medicine, where practitioners are reluctant to use black-box models for critical patient decisions. RESULTS: We introduce CoxKAN, a Cox proportional hazards Kolmogorov-Arnold Network for interpretable, high-performance survival analysis. Kolmogorov-Arnold Networks (KANs) were recently proposed as an interpretable and accurate alternative to multi-layer perceptrons. We evaluated CoxKAN on four synthetic and nine real datasets, including five cohorts with clinical data and four with genomics biomarkers. In synthetic experiments, CoxKAN accurately recovered interpretable hazard function formulae and excelled in automatic feature selection. Evaluations on real datasets showed that CoxKAN consistently outperformed the traditional Cox proportional hazards model (by up to 4% in C-index) and matched or surpassed the performance of deep learning-based models. Importantly, CoxKAN revealed complex interactions between predictor variables and uncovered symbolic formulae, which are key capabilities that other survival analysis methods lack, to provide clear insights into the impact of key biomarkers on patient risk. AVAILABILITY AND IMPLEMENTATION: CoxKAN is available at GitHub and Zenodo.

Humans↗

PubMed1985

Biological factors predisposing to traumatic posterior dislocation of the hip. A selection process in the mechanism of injury.

The factors involved in the mechanism leading to traumatic posterior dislocation of the hip are examined. In 47 adult patients who had previously suffered such a dislocation, ultrasound scans were used to measure femoral anteversion on both the affected and the uninjured side. In 36 normal adult volunteers, used as controls, similar measurements were made. Femoral anteversion on both the injured and uninjured side was significantly reduced in the patients compared with the volunteers. These findings are discussed in the light of previous work which indicates that medial rotation is a factor in the mechanism of posterior dislocation of the hip. It is suggested that reduced anteversion acts like medial rotation to make the hip more susceptible to posterior dislocation, and that the less the anteversion the more likely is the injury to be a dislocation rather than a fracture-dislocation. It is concluded that patients who suffer such dislocated hips belong at one extreme of the normal population, having either reduced femoral anteversion or even retroversion, and that this anatomical feature selects towards hip dislocation rather than to injury of the femoral shaft, knee or tibia during the appropriate type of accident.

Adult↗