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Results for “Similarity network fusion”

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11 recordsLinked to original sources
PubMed2026

Machine learning-enabled multi-omics discovery of prognostic biomarkers and signaling targets in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains difficult to subtype using single omics layers. We conducted an exploratory investigation integrating reverse-phase protein array (RPPA) and DNA methylation data from the cancer genome atlas (TCGA)- pancreatic adenocarcinoma (PAAD) to assess the feasibility of multi-omics subtyping, alongside a supervised machine learning analysis of a small gene expression omnibus (GEO) transcriptomic cohort (n = 26) to identify candidate diagnostic genes. RPPA-based K-means clustering suggested a weak, possible two-subtype structure (silhouette ≈ 0.16) that remained unassociated with overall survival (log-rank p = 0.113) and lacked independent prognostic value. An independently performed similarity network fusion (SNF) analysis integrating RPPA and methylation data showed low concordance with RPPA-derived subtypes (Adjusted Rand Index (ARI) = 0.014), indicating limited convergence between molecular modalities. Supervised machine learning analysis of the GEO cohort using a fully nested leave-one-out cross-validation pipeline achieved a mean (area under the curve) AUC of 0.896 across four classifiers and identified four-fold-stable candidate genes (ESCO2, COL17A1, BCL2L14, and SOWAHB). However, this gene panel demonstrated limited external validity across two independent PDAC cohorts (log-rank p = 0.438 for both GSE62452 and GSE28735), indicating limited generalizability despite robust internal performance. Collectively, these findings provide limited evidence for a robust, prognostically significant multi-omics subtype or a validated diagnostic gene signature; instead, this study serves as a hypothesis-generating resource and highlights the importance of rigorous cross-validation and independent external validation in small-sample transcriptomic biomarker discovery.

Humans↗
PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗
PubMed2025

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources.

MOTIVATION: Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis, and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous, and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature, several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice. RESULTS: We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. miss-SNF is able to recover missing patient similarities and is "task agnostic", in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer's disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available. AVAILABILITY AND IMPLEMENTATION: miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.

Humans↗
PubMed1979

Normal and pseudorabies virus infected primary nerve cell cultures in scanning electron microscopy.

Primary cell cultures from the central nervous system of the embryonic rat were inoculated with pseudorabies virus. Their morphological changes were studied by phase contrast microscopy and by scanning as well as by transmission electron microscopy. Uninfected cultures display two distinct cell layers in scanning electron microscopy: a flat continuous monolayer supports a heterogeneous population of individual, presumably neural cells, which emit processes of different number and size. The latter cells form contacts by a dense network of fibres. Infectious virus is propagated in these nerve cell cultures with similar effectivity as in other cultures. The infectoin leads to fusion and death of the cells. By the time the cytopathic effect is visible, nearly all cells, including those of neuronal and those of nonneuronal appearance, are studded with ample amounts of virus-sized particles. The particles represent viruses as demonstrated by transmission electron microscopy or by treatment with a hyperimmune serum directed against pseudorabies virus structural components. Hyperimmune serum leads to clustering of the particles at the cell surface. The amount of virus particles per surface unit was about 10 times higher on neural cells as compared to primary rabbit kidney cells. The concentration of infectious particles in the supernatant, however was approximately the same. The system described appears to be useful for the study of acute virus effects on neural tissue under strictly controlled conditions.

Animals↗
PubMed1979

The developmental morphology of Torpedo marmorata: electric organ--electrogenic phase.

The electrogenic developmental phase of the electric organ of Torpedo marmorata begins at 40 mm of embryo length and is characterized by a horizontal flattening of the vertically orientated myotubes. The first sign of this process is a rounding up of the ventral poles of the myotubes and a disassembly of the myofibrils located therein. Occurring concomitantly with this is a migration of the nuclei to the cell center which results in a horizontal plane of nuclei. Filament bundles are then found within the ventral cytoplasm often projecting upwards from the ventral plasma membrane. The filaments of the bundles are dimensionally similar to the myofilaments of muscle and it is suggested that the bundles play a role in cellular transformation. In contrast the dorsal pole of the cell appears to be integrated "passively" with the final cell shape as no morphological correlates of a retraction process have been found. A canalicular system, composed of a complex network of irregular tubules and vacuoles, appears just below the dorsal plasma membrane characterizing this region of the cell. A mononucleated satellite cell population lies in close proximity to the dorsal surface of the differentiating cell and fusion between the two cell types occurs throughout development. Cell shape transformation is complete by 55 mm of embryo length and the intercolumnar nerves begin to invade the interelectrocyte space. The ingrowing neurites preferentially course along the ventral electrocyte surface establishing junctions similar to motor endplates.

Animals↗
PubMed1977

A myogenic cell line with altered serum requirements for differentiation.

Dfferentiation properties of a cell line, L84, which originated from a non-fusing clone isolated from the myogenic line L8, are described. In nutritional medium supplemented with 10% serum used routinely with L8 cells, L84 cells continue to proliferate to very high densities and fail to form multinucleated fibres. When grown in medium supplemented with 2% horse serum of 2% horse serum plus 0.1% microng/ml insulin, L84 cells behave very similarly to L8 cells grown in medium supplemented with 10% horse serum: when the cultures reach confluency, proliferation decreases and cells start to fuse and form a dense network of fibres. Large increases in creatine kinase activity and synthesis of myosin are associated with cell fusion. Under conditions in which L84 cells do not fuse the increase in these synthetic activities is not observed, even after extremely high cell densities are reached. The data show that L84 cells retain the programme for their differentiation into muscle fibres. The difference between L84 and its progenitor line L8 lies in the sensitivity to the environmental conditions which trigger the expression of this programme.

Animals↗
PubMed2026

Survival prediction for clear cell renal cell carcinoma based on deep multimodal synergistic survival network.

Objective.To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accurate prognostic analysis for clear cell renal cell carcinoma (ccRCC).Methods.This study (DMSSN) utilized matched multimodal data from the Cancer Genome Atlas-KIRC database, including CT imaging data, whole slide images, copy number variation (CNV) features, and clinical data. Deep Canonical Correlation Analysis was employed to map heterogeneous modalities into a shared latent space. Contrastive learning was introduced to enhance semantic consistency across multimodal features, and a gating network was utilized for the adaptive fusion of multimodal information to achieve precise survival risk prediction for patients.Results.Experimental results demonstrated that DMSSN achieved a Concordance Index (C-index) of 0.8153 ± 0.0994, with a Log-rank testp-value of 1.6553×10-11. DMSSN exhibited significant performance advantages over traditional statistical methods like Log-rank-Cox (0.7055 ± 0.0670) and machine learning methods such as Random Survival Forest (RSF) (0.6836 ± 0.1048). Furthermore, in comparison with similar deep learning approaches, DMSSN outperformed late fusion strategies (0.7493 ± 0.1211) and discrete-time survival models such as DeepHit (0.7655 ± 0.1041) and Nnet-surv (0.7694 ± 0.0635). Notably, DMSSN still achieved the best predictive performance when compared to the classic deep survival model DeepSurv (0.7919 ± 0.0978) and advanced state-of-the-art multimodal fusion frameworks like Context-Aware Transformer (0.7735 ± 0.0818) and Multimodal Co-Attention Transformer (0.8102 ± 0.0972). Ablation studies showed that removing any single modality led to a decline in performance, with the largest numerical decrease occurring after removing CT imaging features (C-index decreased to 0.7327), validating the complementarity of multimodal data and the pivotal role of radiomic features in prognostic assessment. Module ablation experiments further confirmed the effectiveness of the core components.Conclusion:By effectively integrating imaging, pathology, genomic, and clinical features, the DMSSN framework demonstrates superior performance and robustness in the survival prediction of ccRCC.

Carcinoma, Renal Cell↗
PubMed2026

Comparison of Ketamine and Pregabalin on Postoperative Opioid Usage and Pain Management in Spinal Fusion: Systematic Review and Network Meta-analysis.

BACKGROUND CONTEXT: Spinal fusion is associated with substantial early postoperative pain and opioid exposure. Both ketamine and pregabalin are widely incorporated into Enhanced Recovery After Surgery (ERAS) protocols as opioid-sparing adjuncts. However, their comparative efficacy and safety in this specific setting remain uncertain. Our objective was to compare ketamine and pregabalin indirectly for early postoperative opioid consumption, pain, and adverse events in adults undergoing spinal fusion. METHODS: Pubmed, Embase, and Cochrane Trials were searched from inception through October 2025. Eligible studies were randomized trials enrolling adults undergoing instrumented spinal fusion, randomized to perioperative ketamine, pregabalin, or control, and reported extractable 24-hour opioid consumption or pain outcomes. Continuous outcomes were pooled as mean differences in MME or VAS units, and adverse events were reported descriptively. A connected treatment network was analyzed using random-effects models. Risk of bias (RoB) was assessed with the Cochrane RoB 2 tool. RESULTS: Thirteen trials (n=879) were included: ketamine (n=210), pregabalin (n=271), and control (n=398). Six trials contributed opioid data (3 ketamine, 3 pregabalin). Using pregabalin 150 mg as reference, ketamine was associated with lower 0-24-hour opioid use (MD -56.99 mg MME; 95% CI -99.56 to -14.43). Control (MD +21.31; 95% CI -1.05 to +43.66) and pregabalin 300 mg (MD -13.22; 95% CI -40.41 to +13.96) did not significantly differ from pregabalin 150 mg. Seven trials contributed 24-hour VAS data, with control being associated with higher pain versus pregabalin 150 mg (MD +0.84; 95% CI +0.01 to +1.66), while ketamine and pregabalin 300 mg were not k significantly different. Adverse events were generally infrequent and similar to control. CONCLUSIONS: Both ketamine and pregabalin provide early opioid sparing with comparable 24-hour analgesia. Ketamine showed a larger opioid-sparing point estimate, but indirect comparisons are imprecise. Adequately powered head-to-head trials with standardized protocols and adverse event reporting are needed.

Humans↗
PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗
PubMed2026

Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia-Related SNP Manifolds.

Many mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. We propose a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk single nucleotide polymorphisms (SNPs) within a jICA fusion framework, resulting in four parallel fusions in 32,861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 820 individuals (348 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. We observed a mixture of "state-invariant" and "state-variant" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different SNPs involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. By augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into the underlying genetic risk of disease-related alterations in dynamic brain function.

Humans↗
PubMed2026

Azotobacter vinelandii AmrZ is a global regulator linking alginate production and c-di-GMP homeostasis.

Azotobacter vinelandii, a member of the Pseudomonadaceae, produces the exopolysaccharide alginate during vegetative growth; however, the circuitry linking alginate biosynthesis to lifestyle transitions remains poorly defined. Here, we show that the Ribbon-Helix-Helix transcription factor AmrZ coordinates alginate production, intracellular c-di-GMP levels and motility. Deletion of amrZ abolished alginate synthesis, whereas chromosomal complementation restored it. A PalgD-gusA fusion and RT-qPCR demonstrated that algD, the first gene in the alginate biosynthetic cluster, depends on AmrZ for expression. Motif analysis identified multiple AmrZ sites upstream of algD, and electrophoretic mobility-shift assays (EMSAs) confirmed specific binding to these regions. AmrZ also positively autoregulates: PamrZ-gusA activity decreased in ΔamrZ, and purified AmrZ bound the amrZ promoter in EMSA. Moreover, PamrZ activity required the sigma factor AlgU, consistent with the presence of an AlgU promoter; this positive, AlgU-dependent feedback may stabilize AmrZ under alginate-inducing conditions. To probe AmrZ control of c-di-GMP, we implemented a riboswitch-based biosensor in A. vinelandii. The ΔamrZ strain showed a markedly reduced signal, similar to a diguanylate cyclase (DGC) mutant, whereas a phosphodiesterase mutant displayed elevated output, validating the assay. RNA-seq and RT-qPCR identified two DGC genes, AVAEIV_RS11610 and AVAEIV_RS18795, as AmrZ-activated targets; EMSA verified direct binding at the RS11610 regulatory region. By contrast, transcription of the principal vegetative DGC AvGReg was not AmrZ-regulated. Lower c-di-GMP in ΔamrZ correlated with larger swimming halos. Collectively, these genetic, biochemical and transcriptomic data support a model in which AmrZ directly activates algD and elevates c-di-GMP via selected DGCs, thereby promoting alginate synthesis while reducing motility. RNA-seq data also indicate that AmrZ influences broader cellular programmes, including metabolism and iron homeostasis, positioning AmrZ as a central regulator that links c-di-GMP homeostasis to coordinated exopolysaccharide production in A. vinelandii. This work contributes to our understanding of the regulatory networks controlled by AmrZ outside the Pseudomonas genus and reveals important differences in its targets and regulatory mechanisms.

Azotobacter vinelandii↗