Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “who classification”

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 19 recordsLinked to original sources
PubMed1979

Visual classification of banded human chromosomes. III. Classification and karyotyping of density profiles described by band transition sequences.

Band transition profiles (BT-profiles) representing extracted band pattern features of 898 density profiles of banded chromosomes were classified and karyotyped by a cytogeneticist in order to investigate how much information was lost by substituting for the original density profiles their extracted features. The results were evaluated and compared with visual classification and karyotyping of the same 898 density profiles from which the BT-profiles were derived. Six per cent errors were made in classification of isolated BT-profiles and 0.7% errors were made in karyotyping BT-profiles. These error rates were comparable to the corresponding error rates in classifying and karyotyping density profiles, which were 5% and 0.5%, respectively. It is concluded that most of the important band pattern information of the density profiles is retained in the BT-profiles, and it is supposed that the condensed BT-sequences (from which the BT-profiles are derived) constitute a sufficient and appropriate basis for automated karyotyping.

Chromosome Banding↗
PubMed1979

Classification of animal lymphomas: the implications of applying Rappaport's classification for human lymphomas to experimental tumors.

One hundred and seventy animal lymphomas (species ranging from molluses to monkeys) were reclassified histologically according to the modified Rappaport classification for human lymphomas. The results were correlated with the etiology of the lymphomas, their clinical course, and in selected cases with their immunological type. The study stresses the value of such a procedure for comparative reasons, allowing a more adequate selection of animal models for human lymphomas.

Animals↗
PubMed1979

A critical analysis of the classifications of non-Hodgkin's lymphomas.

The Rappaport classification of non-Hodgkin's lymphomas was proposed almost a quarter century ago, before the advent of modern immunology. This classification, which is based entirely on morphologic features, has proved its clinical usefulness. In light of recent scientific advances, however, its terminology is not appropriate. Five new classifications have been proposed recently, each claiming to have more merit than the others. The purpose of this study is to critically analyze and evaluate these newly proposed classifications to determine which classification is conceptually and scientifically acceptable as well as clinically useful. The results of the study show that there are more similarities than differences among the Rappaport. Lukes and Collins, Dorfman, British, and WHO classifications; the Kiel classification, however, is fundamentally different (Tables 8, 9, 11). None of these classifications can be used in its proposed form. Based on the analysis of these classifications, a compromise working classification is proposed which incorporates the relevant concepts and terminology from the Rappaport, Berard, Dorfman, WHO, and Lukes and Collins classifications (Tables 15, 16). The proposed compromise classification is an attempt to reconcile the various classifications, and to stimulate others to offer modifications which may bring about a final solution to the problem of classification of non-Hodgkin's lymphomas.

Burkitt Lymphoma↗
PubMed2026

A unified benchmark of supervised and retrieval-based methods for viral genomic sequence classification.

The rapid growth of genomic sequencing demands fast, accurate, and scalable analysis methods. In viral genomic classification, expanding labeled reference collections can make supervised models costly to update and dependent on fixed label sets, motivating retrieval-based genomic classification as a simpler, more flexible alternative. We present a unified benchmark of supervised and retrieval-based methods for viral genomic sequence classification across three viral classification tasks: hepatitis C virus (HCV) genotyping, COVID-19 discrimination, and human papillomavirus (HPV) genotyping. We compare standard sequence encodings (one-hot, k-mers, FCGR) with dense embeddings (dna2vec, DNABERT). For each representation, we evaluate supervised classifiers (Random Forest, Decision Tree, XGBoost) and retrieval-based classification, where sequence vectors are indexed with FAISS and labels are assigned via similarity-weighted k-NN. Furthermore, we benchmark multiple FAISS index types (Flat, IVF, HNSW, IVFPQ, OPQ) to characterize accuracy-speed-memory trade-offs at scale. The results show that XGBoost and retrieval using Flat or IVF indexes achieve strong classification performance under different computational profiles. Compressed indexes such as IVFPQ and OPQ substantially reduce memory usage, although their accuracy loss depends on the dataset and representation. Overall, supervised XGBoost provides a favorable accuracy-size trade-off, while retrieval-based classification remains competitive and allows labeled reference sequences to be incorporated without retraining a global classifier. This benchmark provides practical guidance for selecting sequence representations, classifiers, and vector-search indexes under different accuracy, memory, and update requirements.

Genome, Viral↗
PubMed1978

Protist classification and the kingdoms of organisms.

Traditional classification imposed a division into plant-like and animal-like forms on the unicellular eukaryotes, or protists; in a current view the protists are a diverse assemblage of plant-, animal- and fungus-like groups. Classification of these into phyla is difficult because of their relatively simple structure and limited geological record, but study of ultrastructure and other characteristics is providing new insight on protist classification. Possible classifications are discussed, and a summary classification of the living world into kingdoms (Monera, Protista, Fungi, Animalia, Plantae) and phyla is suggested. This classification also suggests groupings of phyla into superphyla and form-superphyla, and a broadened kingdom Protista (including green algae, oomycotes and slime molds but excluding red and brown algae). The classification thus seeks to offer a compromise between the protist and protoctist kingdoms of Whittaker and Margulis and to combine a full listing of phyla with grouping of these for synoptic treatment.

Animals↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗
PubMed2026

IASLC Update on Classification of Pulmonary Neuroendocrine Neoplasms.

Since the publication of the 2021 WHO classification of thoracic tumors, our knowledge of pulmonary neuroendocrine neoplasms (NENs) has expanded significantly, particularly through the elucidation of molecular pathways and proposals to refine histopathologic classification. This expanded knowledge across all aspects of pulmonary NENs holds promise for more precise stratification of neuroendocrine tumors (NETs) and the potential development of novel, subtype-specific therapeutic strategies for all NENs. Based on our comprehensive review of the current pulmonary NEN landscape, our multidisciplinary expert panel has deliberated on the modification and updating of the 2021 classification, resulting in the proposal of a new pulmonary carcinoid/NET classification presented in this position paper, which incorporates the following three major points: (1) The proposed framework continues the shift from the traditional carcinoid terminology toward broader adoption of the "NET" nomenclature as found in other organ systems while retaining the term "carcinoid" as the primary diagnostic term to ensure clear communication with thoracic clinical providers. (2) Ki-67 has been incorporated as a diagnostic criterion, aligning with practices in other NET classifications. (3) There is formal recognition of the concept of "carcinoid/NET G3," a rare subset of lung carcinoids characterized by increased proliferative activity but with molecular features more aligned with pulmonary NETs than with high-grade neuroendocrine carcinomas. This position paper on the current knowledge of pulmonary NENs, including the proposed carcinoid/NET classification, will aid in accurate tumor categorization and guide treatment strategies.

Carcinoid tumor↗
PubMed2026

A module-based approach for post-omics, post-GWAS network-based gene classification.

MOTIVATION: Complex traits and diseases are highly polygenic and understanding the full set of genes involved is a central challenge in biomedicine. However, due to sample size limitations and noise (technical and biological), experimental approaches for disease-gene discovery such as transcriptomics and GWAS result in long, noisy, heterogeneous gene lists, which may be trimmed to a subset of likely relevant genes while leaving several false negatives. Computational gene classification approaches, especially those using genome-scale molecular interaction networks, are promising avenues for complementing such experimental findings by analytically expanding observed gene lists based on the functional relatedness between genes. We previously introduced the network-based gene classification approach, GenePlexus, which was rigorously benchmarked to show state-of-the-art performance, especially for predicting novel genes associated with biological processes and fine-grained phenotypes. Network-based gene classification performance,however, declines for diseases, especially when the inputs are omics and GWAS-based long gene lists. RESULTS: Here, we show that these disease gene lists span multiple biological processes spread across the molecular network, and we propose ModGenePlexus, a new network-based gene classification method that takes a two-stage approach. First, clustering and semi-supervised learning decomposes the input gene list into coherent, denoised network gene modules. Then, ModGenePlexus trains supervised (GenePlexus) classifiers for each module and aggregates predictions to return genome-wide rankings. We benchmarked ModGenePlexus across simulated data, transcriptomic signatures, and GWAS datasets (together spanning hundreds of diseases), showing improved recovery of known disease genes compared to GenePlexus. Beyond improved classification, the results of enrichment analysis of ModGenePlexus outputs are much more interpretable by virtue of revealing nuanced biological processes. Together, these results establish ModGenePlexus as a scalable, interpretable tool for gene classification of GWAS- and omics-derived gene lists across diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: ModGenePlexus is freely available on GitHub at https://github.com/krishnanlab/ModGenePlexus, and the full source code and results supporting this study are available on Zenodo at https://zenodo.org/records/19857910.

Genome-Wide Association Study↗
PubMed1978

Classification of lymphomas.

Malignant lymphomas are neoplasms of cells of the lymphoreticular or immune system. Classification of these neoplasms has long been controversial and confusing. In recent years, considerable progress has been made in establishing useful and prognostically significant classifications of lymphomas. Currently, lymphomas may be divided into two main groups: Hodgkin's disease and non-Hodgkin's lymphomas. The Rye classification of Hodgkin's disease is now widely accepted and used throughout most of the world. In contrast, considerable conflict exists about the schemes of non-Hodgkin's lymphomas. The traditional classifications of non-Hodgkin's lymphomas currently used by most pathologists are based purely on morphologic grounds, and, despite the fact that they may be conceptually incorrect, they have often been shown to be useful for clinicopathologic studies. New or modern but yet untested schemes based not only on morphologic criteria, but also on recent immunologic techniques, have been proposed. This work will review the classifications of Hodgkin's disease and the non-Hodgkin's lymphomas, emphasizing the currently used schemes, describe the major modern classifications of lymphomas, and discuss and illustrate the subclasses of lymphomas and the differential diagnoses of the various types of lymphomas from nonlymphomatous proliferations which may mimic them.

Diagnosis, Differential↗