Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Feature selection”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources
PubMed1986

Selected behavioral features of patients with borderline personality traits.

Selected behavioral features felt historically and empirically to be significant in the borderline personality disorder were evaluated in 4,800 psychiatric inpatients. Variables measured included number of hospitalizations and type of discharge, suicidal behavior, physical violence, and outcome after discharge. A statistical analysis was performed to determine the relationship between depth and severity of borderline traits and the aforementioned behavioral features. Results indicated that irregular discharges, frequent suicide attempts, first suicide attempt prior to age 40, violence within and outside the hospital, and gradual deterioration in social and occupational functioning were found significantly more often in patients with high levels of borderline personality traits.

Adult↗
PubMedDate not supplied

Numerical evaluation of cytologic data. III. Selection of features for discrimination.

The proper selection of variables is important in assembling a profile to best describe a given group, whether of patients or cells, vis-à-vis other groups. The need often arises to determine which variables in comparable profiles best discriminate between the profiles. Three techniques for the evaluation and selection of variables on the basis of their potentiality for discrimination are discussed in this article. The Kruskal Wallis test is useful in determining if a certain feature (variable) has any statistical significance between groups. The ambiguity function after Genchi and Mori and the measure of detectability (d') are discussed as direct measurements of a feature's ability to discriminate between groups. Fully worked numerical example suitable for execution on a pocket calculator are given.

Cytological Techniques↗
PubMed2026

A benchmarking study of feature screening approaches across type 1 diabetes omics studies classification settings.

In recent years, high dimensional omics analyses have become more commonplace for investigating complex biological systems. Typically, these studies attempt to identify key biomolecules associated with a particular biological process. Often, machine learning (ML) is used to identify these biomolecules, typically by learning which biomolecules are highly predictive of a treatment, biological outcome, or phenotype. A major challenge of applying ML to high throughput omics is overcoming noise when sample size is limited and unbalanced with respect to tens of thousands of biomolecules measured. Thus, feature selection (the process of reducing the number of predictors) is both a critical and common step in the ML analysis pipeline. While much attention has been given to embedding and wrapping techniques for feature selection in the omics space, filter-based methods for model-free feature selection have appealing theoretical properties. This manuscript evaluates sure screening, a class of filter-based feature selection methods which provide analytical guarantees for true feature set retention. Here, we cover existing feature screening methods based on the sure screening principal, available software, methods to improve feature screening, and contextualize feature screening in the larger discussion of feature selection for omics data analysis. Additionally, a suite of model-free sure screening approaches is applied and compared for several omics biomedical applications in a ML classification context. We identified BcorSIS as the most effective and computationally efficient screening method across various omics datasets, consistently outperforming others like CSIS and DCSIS in runtime.

Humans↗
PubMed1992

Effective outpatient drug treatment organizations: program features and selection effects.

This research identifies program features that predict outpatient drug treatment outcomes. Treatment effectiveness is measured at the organizational level of analysis in a nationally representative sample of non-methadone outpatient drug misuse treatment organizations (N = 394). Multivariate analyses are conducted to identify program features at various stages of the client career that are related to client outcomes after controlling for client characteristics, organizational characteristics, and social area characteristics. Results indicate that effective non-methadone outpatient drug misuse treatment is related to a number of program features including adequate staff levels, quality assurance efforts, and client follow-up, as well as selection factors that reflect client problem severity.

Accreditation↗
PubMedDate not supplied

[Evaluation of selected personality features in patients with various clinical forms of bronchial asthma].

Psychological factors are of importance in the onset and clinical course of the bronchial asthma. Marked emotional disorders are seen in patients with atypical asthma. This study aimed at evaluating selected personality features in patients with various clinical forms of the bronchial asthma. Statistical analysis included 91 asthmatic patients and 30 healthy individuals being a control group. Selected personality features were evaluated with three psychological tests: Eysenck Personality Inventory, Minnesota Multiphasic Personality Inventory, and Cattel's Self cognition Chart. The obtained results have shown that the index of psychopathologies is higher in patients with non-atopic bronchial asthma than in patients with atopic asthma. Therefore, psychotherapy of asthmatic patients, especially with non-atopic form of the disease, should emphasize disturbances of experienced feelings in such patients.

Adult↗
PubMed2025

A comparative study highlights superiority of LSTM in crop genomic prediction.

We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic prediction (GP) has been developed as an important method supporting crop breeding. By utilizing the phenotype values result from GP, breeders could make decisions in the seedling stage that consequently benefit for cost saving. In recent years, machine learning emerged as an efficient technology to solve modeling problems in many fields, including crop breeding. However, numerous modeling approaches have hindered the application of GP since breeders struggle to choose. Therefore, a comprehensively methodological research with guiding significance is extremely necessary. In the present study, we systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods. As for genomic feature processing, we found feature selection (SNP filtering approach) performed better than feature extraction (PCA method). Specifically, the feature relationship dependent methods (GBLUP, RNN, and LSTM) as well as DNN architecture showed superior performance with feature selection. Marker density analysis showed positive correlation with prediction accuracy in a limited threshold. Comparison on effect of population size demonstrated a positive correlation between trait genetic complexity and the optimal population size required. By testing fifteen modeling methods, we found LSTM network displayed superior performance, achieving the highest average STScore (0.967) across six datasets. Further research using all cell states or the latest cell states of LSTM inputs demonstrated its architecture particularly adept with capturing additive and epistatic QTL effects among SNPs. In conclusion, our findings provide basic principles for implementing GP in breeding project to maximize prediction accuracy while maintaining cost-effectiveness.

Plant Breeding↗
PubMed1977

Sequence analysis of oligodeoxyribonucleotides by mass spectrometry. 2. Application of computerized pattern recognition to sequence determination of di-, tri-, and tetranucleotides.

A novel strategy for the sequence analysis of oligodeoxyribonucleotides has been devised which is based upon the analysis of intact underivatized oligonucleotides by mass spectrometry followed by interpretation of the mass-spectral data by computerized pattern-recognition techniques. The pyrolytic and electron-impact conditions of the mass spectrometer permit the cleavage of oligonucleotides of varying chain length and composition, yielding reproducible fragmentations and characteristic m/e values which can be used to reveal purine and/or pyrimidine base sequence information. The selection of optimum features (which are the ratios of peak heights of specific ions, or the linear combination of such ratios) has been done by an interactive feature selection method employing multidimensional k nearest-neighbor analysis and two-dimensional feature-space plots (nonlinear mappings) of the mass-spectral data. Features have been found which allow 100% classification accuracy in predicting the 5' and 3' terminus of all of the dinucleotides commonly found in DNA. Other specific features have been found which indicate adjacent nucleotides within a tetranucleotide. Knowledge of the adjacent nucleotide pairs present, in conjunction with the information as to the 3' or 5' position of the residues in each pair, permits the reconstruction of the sequence of the tetranucleotide.

Base Sequence↗
PubMed1991

Visual selection of features and objects: is location special? A reinterpretation of Nissen's (1985) findings.

Nissen (1985) compared selection by location with selection by color or shape in partial-report experiments. Her analysis of response contingencies when a target was defined in terms of one attribute (location, color, or shape), and when the task was to report the two remaining attributes, suggested a special role for selection by location: It appeared that cross-referencing between color and shape was mediated by location. An alternative interpretation is developed here: The findings are explained by a theory of attention (Bundesen, 1990), in which selection by location is treated on a par with selection by color or shape.

Attention↗
PubMed2025

Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.

INTRODUCTION: Gastrointestinal disorders (GIDs) affect nearly 40% of the global population, with gut microbiome-metabolome interactions playing a crucial role in gastric cancer (GC), colorectal cancer (CRC), and inflammatory bowel disease (IBD). This study aims to investigate how microbial and metabolic alterations contribute to disease development and assess whether biomarkers identified in one disease could potentially be used to predict another, highlighting cross-disease applicability. METHODS: Microbiome and metabolome datasets from Erawijantari et al. (GC: n = 42, Healthy: n = 54), Franzosa et al. (IBD: n = 164, Healthy: n = 56), and Yachida et al. (CRC: n = 150, Healthy: n = 127) were subjected to three machine learning algorithms, eXtreme gradient boosting (XGBoost), Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO). Feature selection identified microbial and metabolite biomarkers unique to each disease and shared across conditions. A microbial community (MICOM) model simulated gut microbial growth and metabolite fluxes, revealing metabolic differences between healthy and diseased states. Finally, network analysis uncovered metabolite clusters associated with disease traits. RESULTS: Combined machine learning models demonstrated strong predictive performance, with Random Forest achieving the highest Area Under the Curve(AUC) scores for GC(0.94[0.83-1.00]), CRC (0.75[0.62-0.86]), and IBD (0.93[0.86-0.98]). These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. CONCLUSION: These findings emphasize the potential of microbial and metabolic profiling in cross-disease characterization particularly for GIDs, advancing biomarker discovery for improved diagnostics and targeted therapies.

Humans↗
PubMed1985

The use of pathologic features in selecting the extent of surgical resection necessary for breast cancer patients treated by primary radiation therapy.

The extent of the surgical resection necessary for breast cancer patients treated by primary radiation therapy is unknown. A simple gross excision of the tumor provides the best cosmetic result, but a wide local resection may be important to prevent local recurrence in some patients. In order to identify patients who are not adequately treated by gross excision of the tumor and radiation therapy, we performed a retrospective clinical-pathologic review of 221 treated women with infiltrating duct carcinoma. There were 53 cases in which the excision specimen showed a constellation of three pathologic features: prominent intraductal carcinoma in the tumor, intraductal carcinoma in the grossly-normal adjacent tissue, and poorly-differentiated nuclei. These cases had a 37% risk of a local recurrence at 6 years compared to eight per cent for all other cases (p less than 0.0001). In cases with all three features, the use of a supplemental dose of radiation to the primary site did not significantly reduce the risk of a local recurrence. Local recurrence at 6 years was 34% in cases with all three features, who received supplemental local radiation, compared to 49% in cases not receiving a supplemental dose (p = 0.28). Survival was also worse for patients with all three features compared to other cases (69% vs. 90% at 6 years, p = 0.002). These results indicate that patients with all three pathologic features have a high risk of local recurrence following gross excision of the tumor and radiation therapy. If primary radiation therapy is selected for these patients, they should first undergo a re-excision of the tumor site in order to be certain that areas of extensive intraductal carcinoma have been adequately resected. Patients whose tumors do not show all three features are adequately treated by gross excision of the tumor prior to radiation therapy.

Breast↗
PubMed1989

A novel approach to electrode signal analysis for glucose determination.

The methodology proposed in this presentation consists in considering the stationary Pt-electrode of an electrocatalytic sensor aimed at glucose measurement together with the reference electrode as a "black box" for which a mathematical model is assumed. The model correlates selected features of the output signal to the concentration of glucose and of interfering substances (urea, amino acids) and to their interactions. The model parameters are experimentally identified. During the measurement, the values of previously selected features of sensor output signal are determined; then they serve as the input data for computation of concentrations of glucose and of interfering substances.

Biosensing Techniques↗
PubMed1989

Selected pragmatic features in Spanish-speaking preschool children.

We assessed Spanish-speaking preschool children for the development of seven language functions and three discourse features. Analyses consisted of spontaneous language samples averaging 136 utterances per child, for 18 subjects between 3:0 and 4:5 (years:months). Data for the frequency of occurrence and the percentage of appropriate usage showed that the preschoolers had established communicative competence for the functions and discourse features. Implications include establishing preliminary guidelines for the development of normal pragmatics in Hispanic preschoolers. We also discuss the 10-item taxonomy as a reliable and clinically useful tool with either English-speaking or Spanish-speaking children.

Central America↗
PubMed1990

Respiratory infections caused by Branhamella catarrhalis. Selected epidemiologic features.

PURPOSE: This work reviewed existing literature pertaining to the epidemiologic aspects of respiratory tract infections caused by Branhamella catarrhalis, examined certain epidemiologic features of B. catarrhalis infections occurring at this facility, and identified relevant areas in need of further study. PATIENTS AND METHODS: Literature dealing with the epidemiology of B. catarrhalis infections was reviewed. Records in this Veterans Administration hospital microbiology laboratory were reviewed and all B. catarrhalis isolates and pure cultures of Hemophilus influenzae and Streptococcus pneumoniae were noted for the January 1986 to June 1989 study period. RESULTS: B. catarrhalis is now recognized as a disease-causing pathogen that is particularly noted for its association with acute otitis media in children and lower respiratory tract infections in adults with underlying cardiopulmonary disease. It was recovered from 2.7 percent of all respiratory specimens submitted over a 42-month period at this Veterans hospital. When compared with H. influenzae and S. pneumoniae, B. catarrhalis was found to be the second most commonly isolated respiratory pathogen. It was frequently found in pure culture (53 percent) or in combination with H. influenzae, gram-negative bacilli, or S. pneumoniae. The seasonal recovery of B. catarrhalis was apparent for the November to May period compared with the June to October period (p less than 0.001). CONCLUSION: B. catarrhalis has emerged as a major respiratory pathogen in pediatric and adult patient populations. There is a distinct seasonal pattern associated with its recovery and reasons for this are unclear. Prevalence studies aimed at identifying colonization rates among "low" and "high" risk groups are needed. The availability of restriction endonuclease analysis as a typing system for B. catarrhalis should favorably impact upon future epidemiologic studies. Many B. catarrhalis isolates produce beta-lactamase, and therapeutic options must reflect this.

Adult↗