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Results for “feature fusion”

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At least 19 recordsLinked to original sources
PubMed2026

SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.

Bladder cancer survival outcomes exhibit significant heterogeneity, influenced by multifaceted factors. While digital pathology-based survival models leveraging artificial intelligence show promise, they often overlook complementary data sources. Conversely, imaging lacks cellular detail, and genomics/proteomics entail complexity and cost. To integrate multidimensional data for enhanced survival prediction, we propose SurvGRN, a multi-feature fusion framework. SurvGRN synergistically combines clinical variables, transcriptomics, and digital pathology slides using a gated residual network architecture. Pathological features are extracted via multiple instance learning, while clinical and transcriptomic data are processed as static inputs. These features are dynamically fused using a long short-term memory (LSTM) network for comprehensive survival risk assessment. Evaluated on 400 bladder cancer patients, SurvGRN significantly outperformed existing methods: improving the C-index by 12.6% over DeepMISL; 20.6% and 7.1% over graph-based models (DeepGraphConv and Patch-GCN); and 5.4% and 4.0% over attention-based approaches (Surformer and HVTSurv). Ablation studies confirmed the contributions of pathology features (extracted via ResNet-50 pre-trained on bladder tissue), clinical/transcriptomic data, and the LSTM fusion. SurvGRN also enabled significant stratification of patients into distinct risk cohorts. This work demonstrates that holistic integration of multi-source data through tailored fusion architectures substantially improves bladder cancer survival prediction.

bladder cancer↗
PubMed2026

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans↗
PubMed2026

Conditional Diffusion Model-Based Method for Annotation of Antibiotic Resistance Gene Properties.

The crisis of bacterial antibiotic resistance, which has led to a decline in the effectiveness of antibiotics originally used to combat bacterial infections, has emerged as an urgent challenge for public health. Antibiotic resistance genes (ARGs) are one of the key reasons for bacteria to develop resistance to antibiotics. Therefore, accurately identifying and annotating the critical properties of ARGs is of great importance for addressing the antibiotic resistance emergency. Although existing deep learning models demonstrate remarkable effectiveness in extracting local features from sequence data, they still face limitations in the capacity to further gain the enriched latent representations within the data. To address the critical challenge of extracting higher-quality representations from ARGs sequence data, we propose a novel ARGs properties annotation method based on the conditional diffusion model which is used to learn latent representations through domain-specific knowledge injection. Specifically, during the conditional information integration phase, we systematically incorporate ARGs' domain knowledge to guide the diffusion process in generating high-quality latent representations. To overcome information redundancy caused by direct concatenation of conditional information and intermediate features, we design a cross-attention mechanism that enables feature fusion between heterogeneous information sources, thereby enhancing further the quality of obtained representations. Experimental results on widely used data sets demonstrate the framework's effectiveness in achieving superior prediction performance compared to existing methods.

Anti-Bacterial Agents↗
PubMed2025

Leveraging protein language models for cross-variant CRISPR/Cas9 sgRNA activity prediction.

MOTIVATION: Accurate prediction of single-guide RNA (sgRNA) activity is crucial for optimizing the CRISPR/Cas9 gene-editing system, as it directly influences the efficiency and accuracy of genome modifications. However, existing prediction methods mainly rely on large-scale experimental data of a single Cas9 variant to construct Cas9 protein (variants)-specific sgRNA activity prediction models, which limits their generalization ability and prediction performance across different Cas9 protein (variants), as well as their scalability to the continuously discovered new variants. RESULTS: In this study, we proposed PLM-CRISPR, a novel deep learning-based model that leverages protein language models to capture Cas9 protein (variants) representations for cross-variant sgRNA activity prediction. PLM-CRISPR uses tailored feature extraction modules for both sgRNA and protein sequences, incorporating a cross-variant training strategy and a dynamic feature fusion mechanism to effectively model their interactions. Extensive experiments demonstrate that PLM-CRISPR outperforms existing methods across datasets spanning seven Cas9 protein (variants) in three real-world scenarios, demonstrating its superior performance in handling data-scarce situations, including cases with few or no samples for novel variants. Comparative analyses with traditional machine learning and deep learning models further confirm the effectiveness of PLM-CRISPR. Additionally, motif analysis reveals that PLM-CRISPR accurately identifies high-activity sgRNA sequence patterns across diverse Cas9 protein (variants). Overall, PLM-CRISPR provides a robust, scalable, and generalizable solution for sgRNA activity prediction across diverse Cas9 protein (variants). AVAILABILITY AND IMPLEMENTATION: The source code can be obtained from https://github.com/CSUBioGroup/PLM-CRISPR.

CRISPR-Cas Systems↗
PubMed1979

Pseudo-Chediak-Higashi anomaly in acute myeloid leukaemia. An electron microscopical study.

2 cases of acute myeloid leukaemia with inclusion bodies are presented. The inclusions were found mainly in the blast cells but could also be encountered in lymphocytes and plasma cells. Cytochemical and ultrastructural studies showed a great resemblance of these inclusions to the ones found in Chediak-Higashi anomaly, i.e., high acid phosphatase activity, varying in size of inclusions from clusters of small granules to hugh inclusion, sometimes found in vacuoles, featuring fusion of lysosomes.

Acid Phosphatase↗
PubMed1978

[Anomalous fusional movements: the sensorimotor aspect of anomalous binocular vision (author's transl)].

It has been demonstrated that, besides sensory adaptation phenomena, sensorimotor phenoma may also develop in concomitant esotropia. The existence of the latter entity can be shown particularly by means of prisms and is interpreted as fusional in origin. It is therefore defined as 'anomalous fusional movements'. The features of these anomalous fusional movements were studied in a group of 30 patients, mainly in order to investigate their finality. Correlations were evaluated between anomalous fusional movements and entities previously described by different names according to the interpretation given them by various authors.

Accommodation, Ocular↗
PubMed2025

soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.

The rapid advancement of spatial multi-omics technologies has opened new avenues for dissecting tissue architecture with unprecedented resolution. However, inherent disparities across omics modalities, such as differences in biological hierarchy and resolution, pose significant challenges for integrative analysis. To address this, we present soFusion, a method for representation learning on spatial multi-omics data that enables automated identification of tissue compartmentalization. soFusion employs a graph convolutional network (GCN) to extract latent embeddings from spatial omics profiles. To simultaneously capture both cross-modality relationships and modality-specific features, we introduce a novel strategy for intra- and inter-omics feature learning. Moreover, modality-specific decoders are designed to preserve the unique information embedded in each omics type. We evaluated soFusion on multiple datasets including gene expression, protein expression, and epigenetic features. Across all benchmarks, soFusion consistently outperformed existing methods in delineating anatomical structures and identifying spatial domains with improved continuity and reduced noise. Collectively, soFusion offers an effective solution for spatial multi-omics integration, substantially enhancing the robustness of spatial domain identification.

Humans↗
PubMed2023

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.

Preprint↗
PubMed1978

Fine structure and development of Sertoli junctions in human testis.

Complicated interdigitation-like junctions between the immature Sertoli cells were frequently observed in prepubertal testis (3--8-year-old). Tight or gap junctions could not be found. Subsurface cisternae appear in the testes of 7 and 8-year-old boys. In pubertal testis (11 to 13 years old), junctional specializations between Sertoli cells are composed of membrane fusions, bundles of microfilaments and associated cisternae. These tight junctions block the deep penetration of lanthanum into the seminiferous tubules. The lanthanum-filled Sertoli junctions show characteristic features of membrane fusions. In the seminiferous tubules spermatocytes were not present at 11 years of age but complete spermatogenesis was noted at 12-13 years of age. It is suggested that the human blood-testis barrier is estabilished shortly before or after the spermatogonia proliferate to give rise to primary spermatocytes.

Adolescent↗
PubMed1978

Arthrogryposis multiplex congenita as part of an inherited symptom complex: two case reports and a review of the literature.

Two first cousins appear to have an autosomal recessive disorder consisting of arthrogryposis multiplex congenita (AMC), pterygium coli, congenital cervical spine fusions, and some features of the Eagle-Barrett syndrome. A review of the literature is summarized including the broad concepts of AMC as well as some syndromes in which AMC is but one manifestation.

Arthrogryposis↗
PubMed2025

Q RadFusion: Hybrid Quantum Classical Radiogenomic Framework for Breast Cancer Diagnosis.

BACKGROUND AND PURPOSE: Breast cancer remains the most common cancer in women worldwide, with early and accurate diagnosis critical for patient survival. Radiogenomics integrates imaging phenotypes with genomic profiles, offering a pathway to precision diagnostics. However, existing classical machine learning models often struggle with the high dimensionality and heterogeneity of multimodal data, leading to issues in calibration and reproducibility. This study presents Q RadFusion, a hybrid quantum-classical framework designed to enhance breast cancer diagnosis by fusing mammography and genomics data. METHODS: Q RadFusion was implemented on two publicly available datasets: CBIS-DDSM (2,600 curated mammography cases, TCIA) and TCGA-BRCA (1,000 genomic profiles, GDC). Imaging preprocessing included bias-field correction, segmentation, and harmonization, while genomic data underwent normalization and imputation. Feature selection was performed using the Quantum Approximate Optimization Algorithm (QAOA), and features were mapped into a quantum Hilbert space using Variational Quantum Circuits (VQC). For multimodal fusion, ResNet encoded mammography features, and a Transformer encoded genomic features. Patient-level and site-held-out splits were used for evaluation. RESULTS: Q RadFusion achieved an AUC of 0.96 and accuracy of 94%, outperforming baselines including CNN-LSTM, ResNet + XGBoost, and multimodal Transformers. Ablation studies confirmed the contribution of quantum components, with optimal performance observed at circuit depth, qubits, and QAOA layers. The model also demonstrated improved calibration and ~ 80% fewer parameters compared to deep fusion networks. CONCLUSION: Q RadFusion demonstrates that hybrid quantum-classical radiogenomic integration can deliver accurate, reproducible, and clinically meaningful diagnostic support for breast cancer, with strong potential for future clinical translation.

Breast Cancer↗
PubMed1979

Membrane alterations and other morphological features associated with polyethylene glycol-induced cell fusion.

Polyethylene glycol (PEG) induces rapid fusion of LM cells. Membrane fusion, as detected by formation of pentalaminar membrane arrays, occurs as early as 1 min after PEG treatment. The entire cell surface arrears to be capable of fusion since fusion occurs in regions where pseudopodia make contact with each other or with a neighbouring cell body and also in areas where cells are in contact along their entire periphery. Cytoskeletal components showed no apparent deleterious effect from PEG treatment or subsequent cell fusion as determined by thin-section EM. Freeze-fracture of monolayer cultures reveals a thermotropic rearrangement of intramembranous particles following PEG treatment.

Animals↗
PubMed2025

Clinical, Morphologic, and Molecular Findings in Neurotrophic Tyrosine Receptor Kinase 3 (NTRK3) Fusion Spitz Neoplasms.

Neurotrophic tyrosine receptor kinase 3 (NTRK3) fusions are a relatively common driver of Spitz neoplasms. This subset of Spitz neoplasms may have smaller cells without the typical abundant glassy eosinophilic cytoplasm seen in most Spitz neoplasms. This can make it difficult to recognize them as belonging to the Spitz family and potentially result in misdiagnosis as melanoma. In this study, we assessed the clinical, morphologic, and genomic features of 60 NTRK3 fusion Spitz neoplasms (13 previously reported and 47 new cases) and performed a comprehensive review of the literature. We identified 5 characteristic morphologic patterns: (1) conventional Spitz nevus (SN) or Spitz tumor (ST), (2) spindle cell nevus of Reed, (3) spindle cell tumor of Reed, (4) dysplastic SN, and (5) exclusively spindle cell variant of SN/ST. The most common fusion partners were MYO5A and ETV6. DNA copy number changes were infrequent (18% of cases), with an average of 1 copy number variant per case. Among 54 cases tested for a TERT promoter mutation, all were negative. One case had a homozygous deletion of 9p21. The majority of cases were diagnosed as SN or Reed nevi (n = 37), rather than ST or Reed tumor (n = 23), and none were diagnosed as Spitz melanoma. Among the 30 patients with outcome data, none experienced recurrence following excision (mean follow-up time was 15 months). NTRK3 fusions can produce morphologic variants of Spitz neoplasms that may be difficult to recognize as belonging to the Spitz family. Familiarity with these morphologic patterns can facilitate identification of the NTRK3 fusion, optimizing classification and distinction from melanoma.

Humans↗
PubMed2025

MO-GCAN: multi-omics integration based on graph convolutional and attention networks.

MOTIVATION: Cancer subtypes play a critical role in disease progression, prognosis, and treatment, making their detection essential for tailoring precision medicine. Studies have shown that multi-omics integration outperforms single-omics approaches in cancer subtyping tasks. However, due to the high-dimensionality of multi-omics data, many existing studies either fail to capture the correlation between true labels and learned features, or lack sufficient capacity to model complex biological representations. These limitations hinder the full potential of leveraging the rich and complementary information embedded in multi-omics datasets. RESULT: We propose a framework that leverages supervised feature learning and classification based on a graph-based learning approach with attention mechanism for cancer subtyping. More specifically, we train graph convolutional network models on each omics dataset to extract latent representations, which are then concatenated to form a comprehensive multi-omics feature embedding. We further develop sample fusion network based on the omics-specific graphs, incorporating the derived features and feeding them into a graph attention model for subtype classification. This two-stage multi-omics framework is applied to eight cancer types, with performance evaluated in terms of test accuracy, training time, macro-averaged precision, recall, and F-score. Experimental results show that the proposed method outperforms state-of-the-art approaches across various cancer types. Additionally, we provide empirical evidence supporting the hypothesis that retaining a limited number of high-confidence edges and utilizing enriched embeddings from intermediate graph neural network layers can improve predictive performance. AVAILABILITY AND IMPLEMENTATION: Data and the code are available at https://github.com/YD-00/MO-GCAN-Updated.git.

Neoplasms↗
PubMed2026

Perineuronal net degradation in aggressive glioblastomas with KANK1::NTRK2 fusions.

BACKGROUND: Approximately 10% of glioblastomas harbor targetable genomic fusions. NTRK2 participates in a variety of fusion events that drive tumorigenesis. Two previous reports have described KANK1::NTRK2 fusions in adult glioblastoma patients with poor survival. METHODS: We performed a retrospective analysis of glioblastoma patients treated at Dartmouth-Hitchcock Medical Center (DHMC) from 2020 to 2025 to identify cases harboring KANK1::NTRK2 fusions. Clinical presentation, treatment, histopathologic features, and outcomes were reviewed. In addition, we conducted GeoMx whole-transcriptome and high-plex proteomic digital spatial profiling of a KANK1::NTRK2-positive glioblastoma and a comparator tumor from a long-term survivor. Candidate biomarkers were orthogonally validated using immunohistochemistry and/or immunofluorescence. RESULTS: Two patients with KANK1::NTRK2 fusion glioblastoma were identified, both demonstrating rapid progression, therapeutic resistance, and survival of less than 7 months. Proteomic profiling showed increased expression and activation of canonical NTRK2 downstream signaling pathways, particularly MEK1/2 and ERK1/2. This was accompanied by upregulation of extracellular matrix remodeling enzymes, including MMP3, MMP14, and ADAM15, along with reduced expression of extracellular matrix-associated transcripts and perineuronal net components in particular compared to a non-fusion glioblastoma. CONCLUSIONS: These limited, hypothesis-generating findings suggest constitutive NTRK2 signaling may promote coordinated extracellular matrix degradation and remodeling, potentially facilitating rapid and aggressive tumor growth and invasion in a subset of glioblastomas.

NTRK gene fusion↗
PubMed2026

Cancer-associated fusion transcripts: mechanisms, functional roles, and clinical implications.

Fusion transcripts are hybrid RNA molecules generated through genomic rearrangements or RNA-level fusion mechanisms. They represent important molecular features of many cancers and can function as oncogenic drivers, diagnostic biomarkers, prognostic indicators, and therapeutic targets. Since the discovery of the BCR::ABL1 fusion in chronic myeloid leukemia, numerous cancer-associated fusion transcripts have been identified across hematologic malignancies and solid tumors. These fusion events encompass diverse biological mechanisms, including constitutively active kinases, aberrant transcription factors, epigenetic regulators, and non-coding fusion RNAs. This review summarizes current knowledge of the mechanisms underlying fusion transcript formation, including genomic rearrangement-dependent and rearrangement-independent processes, as well as fusion circular RNAs. The functional roles of fusion transcripts in cancer biology and their clinical relevance as diagnostic, prognostic, and predictive biomarkers are discussed. In addition, recent advances in fusion transcript detection and characterization are reviewed, including next-generation sequencing, long-read sequencing, single-cell approaches, artificial intelligence-assisted computational methods, and CRISPR/Cas9-mediated strategies for functional modeling and functional validation of fusion transcripts. Despite the rapid expansion of fusion transcript catalogs, the biological and clinical significance of most identified fusion events remains incompletely understood. Future progress will depend on integrating advanced sequencing technologies, artificial intelligence-assisted computational prioritization, and systematic functional validation to distinguish clinically actionable fusion transcripts from biologically neutral events. Such multidisciplinary approaches will be essential for translating fusion transcript research into precision oncology and improving cancer diagnosis, patient stratification, and targeted therapy.

Humans↗
PubMed1978

[Kinetics and ultrastructure of sheep fibroblast fusion induced by polyethylene glycol. Comparison with endogenous cell fusion induced by Visna virus].

The authors compare the fusion of sheep fibroblasts induced by low multiplicities of infection using visna virus and by high concentrations of polyethylene-glycol. In the case of Visna virus cell fusion is of the endogenous type, while fusion induced by polyethylene-glycol is of the exogenous type. The ultrastructural features are discussed for each type of cell fusion. The main differences between the two systems involve the intracellular microfilaments and Golgi apparatus.

Animals↗