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PubMed2025

ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.

Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs. The core of ASGCL is the GraphMorpher module, an innovative component that enhances the input graph structure via strategic node attribute masking and topological pruning. By contrasting the augmented graph with the original input, the model delineates distinct positive and negative sample sets at both node and graph levels. This dual-level contrastive approach significantly amplifies the model's discriminatory prowess in identifying nuanced drug responses. Leveraging a synergistic combination of supervised and contrastive loss, ASGCL accomplishes end-to-end learning of feature representations, substantially outperforming existing methodologies. Comprehensive ablation studies underscore the efficacy of each component, corroborating the model's robustness. Experimental evaluations further illuminate ASGCL's proficiency in predicting drug responses, offering a potent tool for guiding clinical decision-making in cancer therapy.

Humans↗
PubMed2025

A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.

Accurate genome binning is essential for resolving microbial community structure and functional potential from metagenomic data. However, existing approaches-primarily reliant on tetranucleotide frequency (TNF) and abundance profiles-often perform sub-optimally in the face of complex community compositions, low-abundance taxa, and long-read sequencing datasets. To address these limitations, we present MBGCCA, a novel metagenomic binning framework that synergistically integrates graph neural networks (GNNs), contrastive learning, and information-theoretic regularization to enhance binning accuracy, robustness, and biological coherence. MBGCCA operates in two stages: (1) multimodal information integration, where TNF and abundance profiles are fused via a deep neural network trained using a multi-view contrastive loss, and (2) self-supervised graph representation learning, which leverages assembly graph topology to refine contig embeddings. The contrastive learning objective follows the InfoMax principle by maximizing mutual information across augmented views and modalities, encouraging the model to extract globally consistent and high-information representations. By aligning perturbed graph views while preserving topological structure, MBGCCA effectively captures both global genomic characteristics and local contig relationships. Comprehensive evaluations using both synthetic and real-world datasets-including wastewater and soil microbiomes-demonstrate that MBGCCA consistently outperforms state-of-the-art binning methods, particularly in challenging scenarios marked by sparse data and high community complexity. These results highlight the value of entropy-aware, topology-preserving learning for advancing metagenomic genome reconstruction.

canonical correlation analysis↗
PubMed2026

Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.

Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.

Prostatic Neoplasms↗
PubMed2025

scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis.

MOTIVATION: Combining single-cell sequencing with ligand-receptor (LR) analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs naturally represent cell-cell communication events. However, current computational methods cannot yet analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the nonlinear nature of cell-cell communication and the high variability given by the patient-specific single-cell RNAseq datasets. RESULTS: Here, we present scACCorDiON (single-cell Analysis of Cell-Cell Communication in Disease clusters using Optimal transport in Directed Networks), an optimal transport algorithm exploring node distances on the Markov Chain as the ground metric between directed weighted graphs. Benchmarking indicates that scACCorDiON performs a better clustering of samples according to their disease status than competing methods that use undirected graphs. We provide a case study of pancreas adenocarcinoma, where scACCorDion detects a sub-cluster of disease samples associated with changes in the tumor microenvironment. Our study case corroborates that clusters provide a robust and explainable representation of cell-cell communication events and that the expression of detected LR pairs is predictive of pancreatic cancer survival. AVAILABILITY AND IMPLEMENTATION: The code of scACCorDiON is available at https://scaccordion.readthedocs.io/en/latest/. and https://doi.org/10.5281/zenodo.15267648. The survival analysis package can be found at https://github.com/CostaLab/scACCorDiON.su.

Humans↗
PubMed2026

PangyPlot: multi-scale interactive visualization of pangenome variation graphs.

SUMMARY: Pangenome variation graphs integrate multiple samples into a unified representation, mitigating the reference bias inherent to linear genomes. However, these graphs can be large and structurally complex. Existing visualization tools are each confined to a fixed scale of resolution, requiring researchers to switch between multiple tools to examine variation at different levels of detail. PangyPlot is an interactive pangenome browser designed for multi-scale exploration of reference variation graphs from full chromosome to nucleotide-level sequence segments. PangyPlot anchors navigation to linear reference coordinates, organizes variation into hierarchical bubble structures, and uses a force-directed layout engine for automatic node arrangement. AVAILABILITY AND IMPLEMENTATION: An instance preloaded with data is available at https://pangyplot.research.sickkids.ca. Source code and documentation are openly available at https://github.com/strug-hub/pangyplot under the MIT License.

Software↗
PubMed2025

Defining and cataloging variants in pangenome graphs.

Structural variation causes some human haplotypes to align poorly with the linear reference genome, leading to 'reference bias'. A pangenome reference graph could ameliorate this bias by relating a sample to multiple reference assemblies. However, this approach requires a new definition of a 'genetic variant.' We introduce a definition of pangenome variants and a method, pantree, to identify them. Our approach involves a pangenome reference tree which includes all nodes (sequences) of the pangenome graph, but only a subset of its edges; non-reference edges are variant edges. Our variants are biallelic and have well-defined positions. Analyzing the Minigraph-Cactus draft human pangenome reference graph, we identified 29.6 million genetic variants. Most variants (99.2%) are small, and most small variants (73.9%) are SNPs. 3.5 million variants (11.7%) have a reference allele which is not on GRCh38; these variants are difficult to detect without a pangenome reference, or with existing pangenome-based approaches. They tend to be embedded within tangled, multiallelic regions. We analyze two medically relevant regions, around the HLA-A and RHD genes, identifying thousands of small variants embedded within several large insertions, deletions, and inversions. We release an open-source software tool together with a VCF variant catalogue.

Journal Article↗
PubMed2026

Graph-based pan-genome reveals structural and functional diversity across oil palm domestication gradients.

BACKGROUND: Oil palm (Elaeis guineensis Jacq.), the world's most land-efficient oil crop, underpins global vegetable oil supply yet faces mounting constraints from limited expansion, climate stress, and disease pressure. These challenges highlight the urgent need for genomic resources that capture species-wide diversity to support sustainable improvement. While recent reference assemblies have advanced trait discovery, single linear genomes fail to represent the full spectrum of structural and gene-content variation, limiting resolution of agronomic alleles. RESULTS: Here, we constructed a graph-based pan-genome from 30 diverse oil palm assemblies representing wild, semi-domesticated, and commercial accessions. We characterized structural variants, gene presence-absence variation, and copy-number gains, with focusing on functional stratification and resistance gene dynamics. The graph-based pan-genome revealed extensive structural and gene-content variation, including a large conserved core, complemented by shell and unique fractions enriched or biased toward regulatory, stress-responsive, and defense-related functions. Structural variation and duplication-derived copy-number gains contributed substantially to gene-content diversity, with semi-domesticated accessions exhibiting the greatest variability. Resistance gene repertoires showed contrasting patterns: receptor-like kinases remained comparatively stable, whereas the CNL subclass of NLR genes contributed disproportionately to shell-genome variation and duplication-associated turnover. CONCLUSIONS: This graph-based pan-genome provides a curated multi-assembly reference and comparative framework for oil palm genomics. By capturing structural variants, gene-content variations, copy-number gains, and resistance gene dynamics across domestication gradients, it establishes a foundation for future pan-GWAS analysis, functional genomics, and molecular breeding strategies aimed at improving resilience and productivity in this globally important crop.

Arecaceae↗
PubMed2026

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

MOTIVATION: Integrating multi-omics data provides valuable insights into biological processes by capturing information across multiple molecular layers, enabling a comprehensive understanding of complex diseases and driving advancements in precision medicine. However, existing computational methods for multi-omics integration face significant challenges, such as low reliability and poor generalizability, due to the high dimensionality and low sample size nature of omics data. RESULTS: To address these challenges, we present PEARL (Pearson-Enhanced spectrAl gRaph convoLutional networks), a novel deep graph learning method for biomedical classification and functional important omics features identification. PEARL leverages a simple yet effective learning architecture to achieve superior and robust performance in high-dimensional, low-sample-size multi-omics settings. Our results demonstrate that PEARL significantly outperforms existing state-of-the-art methods on both synthetic and real biomedical datasets. Furthermore, applied to Alzheimer's disease (AD) brain multi-omics data, features prioritized by PEARL lead to functionally important genes that demonstrate significant enrichment in AD-related pathways. These findings highlight PEARL's practical utility in biomedical research and its potential to enhance biological interpretability in multi-omics studies. AVAILABILITY AND IMPLEMENTATION: The source code of our computational framework is available at https://github.com/zqq121017/PEARL.

Multiomics↗
PubMed2026

KG-Microbe: Building modular and scalable knowledge graphs for microbiome and microbial sciences.

BACKGROUND: The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environment and human health. Doing so is particularly challenging in the context of microbe-host and microbe-microbe interactions that contribute to health or environmental outcomes. There are thousands of relevant microbial species, and millions of interactions among those microbes and with their environment or host. Integrated information (e.g., about host and microbial physiology, genetics, and metabolism) facilitates deeper understanding of complex mechanisms and helps interpret correlative results. RESULTS: The KG-Microbe construction framework is a novel approach to harmonizing bacterial and archaeal data in the form of a findable, accessible, interoperable, reusable and AI-ready knowledge graph (KG). Starting from a core KG with organismal traits, environments, and growth preferences and the integration of established ontologies, the framework generates a hierarchy of related KGs targeting specific use cases, including the human microbiome in the context of disease, or environmental microbiomes. The framework supports customizable taxa subsets representing communities or clades of interest. Evaluations of the KG-Microbe KGs through a series of competency questions demonstrate the accuracy and effectiveness of the data harmonization, and the utility of the resulting KGs in studies of inflammatory bowel disease and Parkinson's disease. Finally, the predictive and environmental capabilities of the KGs are demonstrated by predicting growth preferences using graph features. CONCLUSIONS: The KG-Microbe framework unifies microbial contexts in a single resource to support integrative analyses across biomedical, host, and environmental domains. KG-Microbe is a flexible, modular enabling technology for humans and machine learning methods to uncover candidate mechanistic explanations of microbial associations.

Microbiota↗
PubMed2025

CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity.

Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, methods for predicting binding affinity currently do not take full advantage of 3D protein information. Here, we present CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug-Target Affinity), which uses an equivariant graph neural network to learn more robust protein representations alongside a standard graph neural network to learn molecular representations to predict drug-target affinity. We augment these representations by incorporating an attention-based mechanism between protein residues and drug atoms to improve interpretability. We show that CASTER-DTA represents a state-of-the-art improvement on multiple benchmarks for predicting drug-target affinity and that it generates novel insights for several related tasks. We then apply CASTER-DTA to create a large resource of the binding affinities of every FDA-approved drug against every protein in the human proteome and make these predictions freely available for download. We also make available a web server for researchers to apply a pretrained CASTER-DTA model for predicting binding affinities between arbitrary proteins and drugs.

deep learning↗
PubMed2026

PheBee: A Graph-Aware System for Scalable, Traceable, and Semantic Phenotyping.

OBJECTIVES: Phenotype-driven workflows in clinical and translational research require standardized ontology-based representation, ontology-aware cohort discovery, and provenance inspection for each assertion. Existing approaches optimize either for semantic traversal or scalable batch analytics, but not both. We describe PheBee, a hybrid system that links semantic assertions to scalable evidence storage via a deterministic identifier, preserving provenance while supporting ontology-aware discovery at cohort scale. MATERIALS AND METHODS: PheBee represents phenotype assertions in a knowledge graph as ontology-linked nodes with clinical modifier context (e.g., negated, family history), and stores supporting evidence records in a scalable row-oriented evidence table for cohort-scale access. The two layers are connected by a deterministic identifier enabling stable joins across repeated ingestions without duplicating high-volume evidence in the graph. We evaluated PheBee using synthetic datasets designed to exercise end-to-end ingestion and query workflows. RESULTS: Functional evaluation validated hierarchical term expansion, qualifier-aware retrieval, duplicate-free assertion handling under re-ingestion, and privacy-conscious management of subjects shared across multiple research projects. At scale (10,000 subjects producing 12M evidence records) PheBee completed ingestion in ~30 minutes and responded to interactive queries within 6 seconds under concurrent load. DISCUSSION: PheBee exposes a unified API for ontology-aware cohort discovery with hierarchical term expansion, subject-centric retrieval of phenotypes and clinical modifiers, and evidence and provenance queries. Its data model aligns with GA4GH Phenopackets, facilitating interoperability with phenotype exchange standards. CONCLUSION: By combining ontology-aware semantics with scalable, provenance-bearing evidence storage, PheBee provides a practical open-source foundation for phenotype-driven research workflows that demand both semantic precision and cohort-scale traceability.

cohort studies↗
PubMed2025

monarchr: an R package for querying biomedical knowledge graphs.

SUMMARY: Biomedical knowledge graphs (KGs) aggregate and provide a wealth of information, linking genes and their variants, diseases, phenotypes, and much more. While these data are available in raw and API-hosted form, to date, functionality for working with KGs in the R programming language has been limited. We introduce monarchr, a package for querying and manipulating KG data. Support for the expansive Monarch Initiative KG is built in, and monarchr can accommodate any KG in the Knowledge Graph eXchange (KGX) format. This tidy-inspired interface offers researchers an intuitive, iterative approach to querying and visualizing KG data. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, and installation instructions are available at https://github.com/monarch-initiative/monarchr.

Software↗
PubMed2026

EPIC: Event Prototyping via Information Constrained graph learning for personalized cancer driver gene prediction.

MOTIVATION: Precision oncology relies on accurately distinguishing patient-specific driver mutations from the vast background of passenger alterations. While graph-based computational methods have emerged as powerful tools for this task, they often struggle to preserve the distinct genomic context of individual mutations within complex biological networks. Consequently, subtle patient-specific driver signals are frequently obscured by dominant topological patterns, critically impeding the identification of individualized oncogenic events essential for personalized cancer therapy. RESULTS: To address this, we propose EPIC, a novel framework for Event Prototyping via Information Constrained Graph Learning. Unlike traditional node-centric approaches, EPIC redefines driver prediction as a metric learning task in an event embedding space. We introduce an information-constrained learning strategy that imposes explicit geometric constraints on feature variance, effectively preventing feature collapse and ensuring that low-frequency driver signals are distinctively preserved. Experiments on large-scale cancer cohorts demonstrate that EPIC significantly outperforms established baselines. Notably, the model prioritizes low-frequency driver variants typically overlooked by population-based methods, mapping them to critical oncogenic mechanisms associated with drug resistance and metastasis. Furthermore, clinical actionability analysis confirms that EPIC substantially expands the patient population eligible for targeted therapies. EPIC provides a robust and context-aware solution for personalized cancer driver discovery, bridging the gap between genomic data and actionable therapeutic insights. AVAILABILITY AND IMPLEMENTATION: The source code and datasets are available at https://github.com/spcho-dev/EPIC.

Humans↗
PubMed2026

VirBinn improves viral genome binning from metagenomic Hi-C through graph diffusion.

MOTIVATION: Metagenomic Hi-C provides in situ proximity signals that can improve genome binning and enable virus-host-association analysis. However, viral genome recovery remains difficult because virus-virus Hi-C contact matrices are extremely sparse. Viral genomes are small, often low-abundance, and frequently assemble into short contigs, leaving many true within-genome links unobserved and causing viral bins to fragment. RESULTS: We present VirBinn, a graph-diffusion framework for viral binning from metagenomic Hi-C. VirBinn enhances virus-virus connectivity through two complementary mechanisms: random-walk-with-restart enhancement on the sparse virus-virus contact graph and host-guided diffusion that propagates viral seeds through the host network to infer indirect virus-virus associations. The enhanced views are integrated and clustered using Leiden community detection to produce viral metagenome-assembled genomes (vMAGs). On dataset-specific simulation benchmarks with ground truth, VirBinn consistently recovers more high-quality vMAGs than Hi-C-based and shotgun-based baselines and substantially increases the number of near-complete genomes. On four real metagenomic Hi-C datasets spanning human gut, pig gut, sheep gut (long-read assembly), and wastewater, VirBinn yields more high-completeness vMAGs under CheckV and produces bins with strong within-cluster contact support. Finally, host linkage analysis using reconstructed host MAGs reveals habitat-specific host-association patterns and plausible host taxonomic profiles. AVAILABILITY AND IMPLEMENTATION: VirBinn is available at https://github.com/dyxstat/VirBinn. The scripts to reproduce the results and figures in this article are available at https://github.com/dyxstat/Reproduce_VirBinn.

Genome, Viral↗
PubMed2026

GT-Mamba: a Topology-Aware Graph-State space model for robust and interpretable epigenetic age prediction.

MOTIVATION: Current epigenetic clocks face a trade-off between predictive accuracy and biological interpretability, often relying on dataset-specific correction to generalize across cohorts. We propose GT-Mamba, a novel architecture that integrates a Structure-Aware Graph Transformer with the Mamba state space model. This design captures CpG topological correlations and genome-wide long-range dependencies. RESULTS: GT-Mamba demonstrates strong out-of-the-box robustness across heterogeneous independent validation cohorts, achieving a weighted average MAE of 4.43 years. Notably, it effectively generalizes to EPIC 850k arrays despite partial feature missingness, and maintains consistent performance across homologous age distribution shifts (MAE 2.94 years in a young cohort). Ablation studies confirm that graph topology contributes to improved robustness against noise. Mechanistic analysis suggests that the model captures methylation patterns associated with both developmental and functional processes. AVAILABILITY: Source code and pre-trained models are freely available at https://github.com/NENUBioCompute/GT-Mamba and archived on Zenodo (DOI: 10.5281/zenodo.19703155).

Epigenesis, Genetic↗
PubMed2025

Reference-Free Variant Calling with Local Graph Construction with ska lo (SKA).

The study of genomic variants is increasingly important for public health surveillance of pathogens. Traditional variant-calling methods from whole-genome sequencing data rely on reference-based alignment, which can introduce biases and require significant computational resources. Alignment- and reference-free approaches offer an alternative by leveraging k-mer-based methods, but existing implementations often suffer from sensitivity limitations, particularly in high mutation density genomic regions. Here, we present ska lo, a graph-based algorithm that aims to identify within-strain variants in pathogen whole-genome sequencing data by traversing a colored De Bruijn graph and building variant groups (i.e. sets of variant combinations). Through in silico benchmarking and real-world dataset analyses, we demonstrate that ska lo achieves high sensitivity in single-nucleotide polymorphism (SNP) calls while also enabling the detection of insertions and deletions, as well as SNP positioning on a reference genome for recombination analyses. These findings highlight ska lo as a simple, fast, and effective tool for pathogen genomic epidemiology, extending the range of reference-free variant-calling approaches. ska lo is freely available as part of the SKA program (https://github.com/bacpop/ska.rust).

Polymorphism, Single Nucleotide↗
PubMed2026

SwinePan for pig graph-based pangenome and multiomics data mining.

Pigs are one of the most important livestock species worldwide. Although multiple high-quality reference genomes exist, reliance on a single linear reference limits the detection of structural variants (SVs) and the characterization of population-specific genetic diversity. To address this limitation, we developed SwinePan, a comprehensive and integrated multiomics database for pigs built on a graph-based pangenome framework. SwinePan incorporates a variome derived from the graph-based pangenome, covering 2,598 individuals across 35 breeds, including 185,759 SVs, 117 million SNPs, and 6.8 million indels. The database also integrates transcriptomic data from liver, loin muscle, abdominal fat, and backfat, along with over 150,000 phenotypic records. The online toolkit deployed in SwinePan enables genome-wide association studies (GWAS), expression quantitative trait locus (eQTL) mapping, and colocalization, while interactive modules visualize population structure and multiomics associations, streamlining candidate gene and variant exploration. Additionally, two proof-of-concept analyses demonstrate how SwinePan pinpoints trait-associated loci and deciphers their potential regulatory mechanisms.

Journal Article↗
PubMed2025

HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.

MOTIVATION: Accurate tumor subtype diagnosis is crucial for precision oncology, yet current methodologies face significant challenges. These include balancing model accuracy with interpretability and the high costs of generating multi-omics data in clinical settings. Moreover, there is a lack of validated models capable of classifying hierarchical tumor subtypes across a comprehensive pan-cancer cohort. RESULTS: We present a graph neural network, HallmarkGraph, the first biologically informed model developed to classify hierarchical tumor subtypes in human cancer. Inspired by cancer hallmarks, the model's architecture integrates transcriptome profiles and gene regulatory interactions to perform multi-label classification. We evaluate the model on a comprehensive pan-cancer cohort comprising 11 476 samples from 26 primary cancers with 405 subtypes up to eight levels. The model demonstrates exceptional performance, achieving 5-fold cross-validation accuracy between 85% and 99% for tumor subtypes labeled with increasing details of genomic information. It also shows good generalizability on a validation dataset of 887 samples, assessed using three metrics that consider tumor subtypes at individual, combined, and sample levels. Benchmarking and ablation experiments show that hallmark-based embeddings slightly influence model performance, while the integrated multilayer perceptron plays a significant role in determining classifier accuracy. Additionally, we use the SHAP method to link cancer hallmarks with genes, identifying key features that influence model decisions. Our findings present a biologically informed machine learning framework capable of tracking tumor transcriptomic trajectories and distinguishing inter- and intra-tumor heterogeneity in pan-cancer. This approach holds promise for enhancing cancer diagnostics. AVAILABILITY AND IMPLEMENTATION: HallmarkGraph is accessible at https://github.com/laixn/HallmarkGraph.

Humans↗