[Classifiable and non-classifiable mucopolysaccharidoses].
Explore the source record for details and available documents.
Adolescent↗
SEARCH · Search PubMed
Results for “classifier”
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.
Adaptive classifiers for dicentric chromosomes.
Classification of dicentric chromosomes in a practical automatic screening system comprises three stages. The first generates plausible centromere candidates from each chromosome in an automatically segmented metaphase, and uses contextual knowledge to generate distributions of "probably true" and "probably false" centromeres, thus adapting to the conditions within a particular metaphase. The second stage classifier uses these distributions to re-classify the candidates as centromeres or non-centromeres. From this classification, likely dicentrics are found by counting centromeres; a third classifier attempts to reject false positives among the likely dicentric chromosomes, by comparing the feature values of the proposed centromeres of a chromosome and rejecting chromosomes for which these values do not satisfy certain similarity criteria. The second stage classifier may be a simple box classifier, or may use a variety of parametric Bayesian methods. The performance of these alternatives has been tested both on reference data sets comprising about 600 metaphases, and on larger data sets when embedded in a practical fully automatic dicentric pre-screening system. When operating parameters were such that a similar number of true positives were found by both classifiers, the Bayesian classifier produced about half as many false positive errors as the box classifier, with the final false positive rate being in the region of one candidate dicentric chromosome in every four cells.
Chromosome Aberrations↗
WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.
BACKGROUND: Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset. METHODS: We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson's Disease. RESULTS: Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings. CONCLUSION: We have created a ready to deploy tool, that can classify variants linked with Wilson's disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.
Hepatolenticular Degeneration↗
sWGS Identifies a Copy-Number-High Subset of TP53-mutated Multiple-Classifier Endometrial Carcinomas With Adverse Clinicopathological Features.
TP53-mutated "multiple-classifier" endometrial carcinomas represent a diagnostically challenging subgroup within current molecular classification algorithms. Although these tumors are assigned to POLE-mutated or mismatch repair-deficient categories according to current ESGO/FIGO-based algorithms, their biological heterogeneity remains incompletely characterized. Herein, we retrospectively analyzed TP53-mutated multiple-classifier endometrial carcinomas identified through routine molecular profiling at our institution between 2022 and 2025 using an integrated histopathological, immunohistochemical, targeted sequencing, and shallow whole-genome sequencing approach. Copy-number alteration-high (CNA-high) status was defined as ≥5 large-scale genomic alterations, corresponding to copy-number gains or losses ≥3 Mb within a single chromosomal arm excluding whole-arm alterations. Among 33 analyzable TP53-mutated multiple-classifier endometrial carcinomas, sWGS identified 12 CNA-high tumors (36.4%) and 21 CNA-low tumors (63.6%). CNA-high tumors were more frequently non-endometrioid, high-grade, and advanced-stage according to FIGO 2023. They showed higher TP53 variant allele frequencies (VAF) and higher TP53 VAF-to-tumor-cellularity ratios. After a median follow-up of 12.8 months, recurrences (6/33; 18.2%) and disease-related deaths (3/33; 9.1%) were observed in the CNA-high subgroup, whereas no recurrence or disease-related death was observed among CNA-low patients. These findings indicate that TP53-mutated multiple-classifier endometrial carcinomas comprise biologically distinct subsets that are not fully captured by current 4-tier TCGA-based molecular classification and ESGO-based risk stratification. In this cohort, sWGS identified a CNA-high group with adverse clinicopathological features and clinical events suggesting a potentially more aggressive clinical course. Integration of genome-wide copy-number profiling may therefore refine the biological interpretation of TP53 alterations in multiple-classifier endometrial carcinomas and warrants validation in larger multicenter cohorts.
TP53↗
A hybrid classifier for automated radiologic diagnosis: preliminary results and clinical applications.
We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance (MR) images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with his best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. This system consists of a preprocessing module; a semiautomatic, reliable procedure for obtaining objective estimates of an expert's opinion of an image's tissue composition; a classification module based on a combination of the maximum-likelihood (ML) classifier and the isodata unsupervised-clustering algorithm; and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. Furthermore, the combination of a clustering algorithm and a statistical classifier provides advantages not found in systems using either method alone.
Algorithms↗
SKiM: accurately classifying metagenomic ONT reads in limited memory.
MOTIVATION: Oxford Nanopore Technologies' devices, such as MinION, permit affordable, real-time DNA sequencing, and come with targeted sequencing capabilities. Such capabilities create new challenges for metagenomic classifiers that must be computationally efficient yet robust enough to handle potentially erroneous DNA reads, while ideally inspecting only a few hundred bases of a read. Currently available DNA classifiers leave room for improvement with respect to classification accuracy, memory usage, and the ability to operate in targeted sequencing scenarios. RESULTS: We present SKiM: Short K-mers in Metagenomics, a new lightweight metagenomic classifier designed for ONT reads. Compared to state-of-the-art classifiers, SKiM requires only a fraction of memory to run, and can classify DNA reads with higher accuracy after inspecting only their first few hundred bases. To achieve this, SKiM introduces new data compression techniques to maintain a reference database built from short k-mers, and treats classification as a statistical testing problem. AVAILABILITY AND IMPLEMENTATION: SKiM source code, documentation, and test data are available from: https://gitlab.com/SCoRe-Group/skim.
Metagenomics↗
Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.
BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.
Pseudouridine↗
Classifying by dimensions and reading: a comparison of the auditory and visual modalities.
Children have difficulty learning to read alphabetic writing systems, in part, because they have difficulty segmenting spoken language into phonemes. Young children also have difficulty attending to the individual dimensions of visual objects. Thus, children's early difficulty in reading may be one sign of a general inability to selectively attend to the parts of any perceptual wholes. To explore this notion, children in kindergarten through fourth grade (Experiments 1, 3, and 4) and adults (Experiment 2) classified triads of spoken syllables and triads of visual objects. Classifying speech by common parts was positively related to reading and spelling ability (Experiments 1 and 4), but usually not to classifying visual stimuli by common parts under free classification instructions (Experiments 1 through 3). However, classification was more consistent across the visual and auditory modalities when the children were told to classify based on a shared constituent (Experiment 4). Regardless of instructions, performance on the visual tasks did not usually relate to reading and spelling skill. The ability to attend selectively to phonemes seems to be a "special" skill--one which may require specific experiences with language, such as those involved in learning to read an alphabetic writing system.
Attention↗
The classifier problem in Chinese aphasia.
In recent years, research on the relationship between brain organization and language processing has benefited tremendously from cross-linguistic comparisons of language disorders among different types of aphasic patients. Results from these cross-linguistic studies have shown that the same aphasic syndromes often look very different from one language to another, suggesting that language-specific knowledge is largely preserved in Broca's and Wernicke's aphasics. In this paper, Chinese aphasic patients were examined with respect to their (in)ability to use classifiers in a noun phrase. The Chinese language, in addition to its lack of verb conjugation and an absence of noun declension, is exceptional in yet another respect: articles, numerals, and other such modifiers cannot directly precede their associated nouns, there has to be an intervening morpheme called a classifier. The appropriate usage of nominal classifiers is considered to be one of the most difficult aspects of Chinese grammar. Our examination of Chinese aphasic patients revealed two essential points. First, Chinese aphasic patients experience difficulty in the production of nominal classifiers, committing a significant number of errors of omission and/or substitution. Second, two different kinds of substitution errors are observed in Broca's and Wernicke's patients, and the detailed analysis of the difference demands a rethinking of the distinction between agrammatism and paragrammatism. The result adds to a growing body of evidence suggesting that grammar is impaired in fluent as well as nonfluent aphasia.
Aphasia, Broca↗
Individual differences in the selection and use of features for classifying visual stimuli.
Two experiments were performed to investigate the classification of pot-like outlines by human judges. In experiment 1, seventy-two pot-like shapes, drawn by using all possible combinations of values of four pot ratios, were classified by twenty subjects and by a computer program. The shapes varied only in quantitative features and possessed no all-or-none characteristics. In experiment 2,256 shapes traced from drawings of existing pots were classified by fifteen judges. The pots varied in both quantitative and all-or-none features. The results showed that there were differences between judges in the weightings they assigned to different features, and the judges themselves could be classified according to the weightings they gave the features. There were even differences in the way different judges used all-or-none features for classifying. Possible mechanisms are suggested for the basis of these differences.
Adult↗
[Left atrial myxomas: clinical characteristics, evaluation and considerations in classifying tumors].
Eleven patients underwent surgical excision for left atrial myxomas. Clinical symptoms, coronary angiographic findings and operative procedures were evaluated. Myxomas were classified into two types based on macroscopical findings, and clinical characteristics of these two types were analyzed. Seven cases (64%) classified as "lobular-type myxomas" were seen as lobulated, gelatinous and fragile. Four cases (36%) were classified as "round-type myxomas" were round and elastic soft. Primary symptoms included dyspnea on exertion in five cases (45%) and neurological disturbances in six cases (55%). Brain emboli were found in four patients by CT scan, and were classified as lobular-type myxomas. These eleven myxomas successfully removed in all cases. Four of these myxomas, which were pedunculated with fine fibrous stalks, were shaved along the base at the atrial septum or free wall. Others were excised completely along with a portion of the adjacent septum. Microscopic examination of the operative specimens revealed that two lobular-type myxomas with broad-based attachment to left atrial septum had invaded the atrial septum. All patients are doing well and have had no signs of myxoma recurrence at postoperative periods ranging from 10 months to 12 years (mean follow-up 5.3 years). Seven patients underwent selective coronary angiography due to a diagnosis of a coronary artery disease. All coronary angiograms were normal in all cases. In five (71%) of these seven, abnormally dilated atrial branches were seen as supplying the tumor. In two cases with round-type myxomas, neovascularity was evident and was made up of clusters of tortuous vessels with tumor blush.(ABSTRACT TRUNCATED AT 250 WORDS)
Adolescent↗
Chronobiologic pattern discrimination of plasma hormones, notably DHEA-S and TSH, classifies an expansive personality.
Methods of chronobiologic pattern discrimination, a "monotest' involving the leave-one-out technique and a "maximin distance' algorithm for cluster analysis, along with stepwise discriminant analysis procedures and correlation techniques are used in the search for classifiers of personality. By these methods, chronoendocrine relations of an expansive personality are explored in data sampled around the clock in 4 seasons on 10 North American and 3 Japanese women. Of 12 plasma hormones determined in each season, DHEA-S shows a time-dependent relation to an expansive, egocentric personality assessed by an abbreviated version of the Minnesota Multiphasic Personality Inventory; time-dependent co-classifiers are TSH, LH, prolactin and cortisol. Cost-effective recommendations for sampling are thus proposed for further tests of endocrine factors classifying for an expansive personality and, perhaps, classifying also for any associated vulnerability or risk state.
Adolescent↗
[Perception of schizophrenic patients in classifying pictures of facial expression].
The purpose of this study was to investigate the perception of schizophrenics in classifying pictures of various facial expressions. Schizophrenics were divided into five groups according to the duration of their hospitalization. In the first experiment, subjects were instructed to look at the pictures of three different kinds of facial expressions, anger, delight (laughing) and sadness (crying), and classify them into any categories they like. In comparison with normals, schizophrenics had a difficulty in recognizing the differences in the various facial expressions. In the second experiment, subjects were instructed to look at the same pictures and classify them into three groups of different facial expressions. In this case, schizophrenics were able to classify them almost as well as normals.
Emotions↗
The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups.
T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expression definitions are lacking. To establish a gene-expression-anchored framework for T-ALL subtyping, we aggregated 2314 transcriptomes (15 cohorts, age: 0.8-90.8 years). An extended unsupervised approach defined 17 main clusters and 3 subclusters in samples with high blast fractions. Supervised analyses added an overarching immature T-ALL (early T cell precursor [ETP]-like) definition and resolved the LMO2 γδ-like subtype. All clusters contained samples from at least two cohorts. Characteristic genomic driver enrichments were consistent across cohorts, while gene-expression clusters did not correspond exclusively to single driver events but also reflected developmental origins. A machine-learning classifier based on ALLCatchR, our B-cell acute lymphoblastic leukemia (B-ALL) classifier, identified these 20 transcriptomic subtypes and the immature T-ALL (ETP-like) signature with 0.995-1.0 accuracy in a validation set (n = 203). Testing the classifier on a second hold-out data set (n = 265 samples) showed that 92.7% of predictions matched with corresponding driver alterations. Across all samples, 83.2% of cases received high-confidence predictions, 7.3% candidate predictions, and 9.5% remained unclassified, largely because of low blast fractions. We identified a novel gene-expression cluster markedly enriched (P < 0.001) for clonal hematopoiesis mutations (IDH2 R140Q, DNMT3A) and a stem-/progenitor cell-like gene expression. This novel clonal hematopoiesis-related T-ALL subtype was observed in six cohorts and accounted for 8.9% of adults and 39.5% of patients aged >50 years. We extended ALLCatchR into ALLCatchR2, a free R package that now enables B-/T-lineage separation, gene-expression subtyping, blast estimation, and developmental annotation to harmonize T-ALL classification across studies and clinical contexts.
Journal Article↗
Use of neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients.
The electroencephalographic (EEG) analog signal is complex and cannot easily be described by univariate variables. Clear visual changes in the EEG power spectrum can be present with little or no change in univariate variable values. A method that could produce a single value based on the total data available in the EEG power spectrum would be very useful in monitoring EEG changes. Neural network analysis is a technique that can take multiple inputs and produce a single output value using complicated processing patterns that require training to establish. We examined the usefulness of a series of neural network models to classify 63 EEG patterns against sedation level in 26 mechanically ventilated patients requiring midazolam for long-term sedation. During a stable period of sedation, a 4- to 60-minute period of EEG data was obtained concurrently with a sedation level from 1 (follows commands) to 7 (no or gag response to suctioning of the endotracheal tube). The EEG power spectrum was divided into equal frequency bands, and the log absolute powers in each of these bands were used as inputs for a series of neural network models. The output target was the sedation level associated with each set of EEG data. Networks were trained on a subset of EEG power/sedation score data pairs, and the ability to classify the remaining data pairs was tested. Using a t-test comparison with a random set of sedation levels, we found that trained neural network models classified EEG patterns against sedation level successfully (p less than 0.001).(ABSTRACT TRUNCATED AT 250 WORDS)
Adolescent↗
Sensitivity and specificity of current methods for classifying morbid obesity.
This study examined the sensitivity and specificity of current methods for classifying morbid obesity in females. Results suggest that current methods for classifying morbid obesity (greater than or equal to 45.5 kg over ideal weight or BMI greater than or equal to 45) do not provide acceptable specificity and sensitivity, respectively. We suggest that additional measurements such as total body fatness determined by hydrodensitometry be used to classify morbid obesity and determine eligibility for aggressive therapeutic interventions for weight loss.
Adult↗
Later rather than sooner: extralinguistic categories in the acquisition of Thai classifiers.
An experimental elicitation task with children between the ages of 1;8 and 11;3 shows that children learning Thai numerical classifiers begin with purely distributional information: specifically, (1) that classifiers must appear in the post-numeral position, and (2) that classifiers comprise a conventional, closed set of words. Semantic organizing features, such as salient features of the head noun's referent, appear later than these syntagmatic organizing features. Use of such semantic information is not an immature 'first guess' at grammatical categories, but rather, a necessary component of adult linguistic competence, because the categories are productive both for older children and for adults.
Child↗
The effects of distinctiveness in recognising and classifying faces.
In an earlier study it was found that distinctive familiar faces were recognised faster than typical familiar faces in a familiarity decision task. In the first experiment reported here this effect was replicated with the use of celebrities' faces rather than personally familiar faces. In the second and third experiments the effect of distinctiveness was found to reverse if the task was to distinguish between faces and jumbled faces. Subjects took longer to classify distinctive faces as faces than they did to classify typical faces. Thus distinctive faces were recognised faster, but were classified as faces more slowly than were typical faces, both when personally familiar faces and when famous faces were used as stimuli. These results are interpeted as evidence that faces are encoded by reference to a general face prototype.
Decision Making↗