Briefings in bioinformatics最新文献

{"title":"AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.","authors":"Bekir Altas, Polymnia Georgiou, Anna Onisiforou","doi":"10.1093/bib/bbag568","DOIUrl":"10.1093/bib/bbag568","url":null,"abstract":"<p><p>The convergence of artificial intelligence (AI) and bioinformatics is reshaping the landscape of precision medicine in brain diseases, particularly in neurological diseases and brain tumors. Despite advances in both fields, the combined application of radiomics and multi-omics fusion remains underutilized, even though integrative frameworks are essential for addressing the complexity and heterogeneity of imaging and molecular data. This review examines how integrative AI-bioinformatics frameworks enable the fusion of radiomics with genomics, transcriptomics, proteomics, and other omics modalities to more comprehensively model disease complexity. Leveraging deep learning, graph neural networks, and advanced representation learning, these multimodal systems facilitate biomarker discovery, patient stratification, and personalized treatment strategies. We critically evaluate key challenges including data heterogeneity, limited interpretability, and barriers to clinical validation, and highlight emerging solutions aimed at enhancing reproducibility and translational readiness. Case studies in gliomas, Alzheimer's disease, and multiple sclerosis illustrate the clinical utility of unified imaging-omics architectures. Ultimately, this review positions radiomics and multi-omics integration as a foundational approach for the next generation of precision medicine in brain diseases by bridging the gap between computational innovation and clinical application.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SpaHDSRL: hierarchical dual-graph self-supervised representation learning for integrating spatially resolved multi-omics data.","authors":"Xiang Li, Kangkang Zhang, Yifei Li, Fangrong Yan, Bosheng Li, Qian Ding","doi":"10.1093/bib/bbag517","DOIUrl":"10.1093/bib/bbag517","url":null,"abstract":"<p><p>Spatial multi-omics technologies facilitate simultaneous measurement of multiple molecular modalities within their native spatial context, offering opportunities to characterize tissue organization and cellular heterogeneity. However, effective integration remains challenging because such data concurrently encode spatial adjacency and molecular similarity, while also being limited by high dimensionality, sparsity, noise, and cross-modality heterogeneity. Here, we propose SpaHDSRL, a hierarchical dual-graph self-supervised representation learning framework for spatial multi-omics integration. SpaHDSRL jointly models a shared spatial graph and modality-specific feature graphs, which are integrated through an adaptive gated hierarchical fusion strategy to learn coherent and informative latent representation. To further enhance representation quality, SpaHDSRL combines a Deep Graph Infomax-based objective with spatial regularization, preserving both global informativeness and local spatial consistency. Experiments on simulated and real datasets demonstrate that SpaHDSRL consistently achieves superior performance over existing methods in both the accuracy and robustness of spatial domain identification. Downstream analyses further highlight its utility in marker discovery, functional enrichment, second-modality-associated analysis, and cell-cell communication inference, underscoring its value for dissecting tissue architecture, developmental programs, and multicellular interactions in complex biological systems. The source code of SpaHDSRL is available at https://github.com/Lisa62103/SpaHDSRL.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148988097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets.","authors":"","doi":"10.1093/bib/bbag462","DOIUrl":"10.1093/bib/bbag462","url":null,"abstract":"","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148886320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Halo: a pretrained model for whole-cell segmentation from nuclei images in spatial transcriptomics.","authors":"Xingyuan Zhang, Haotian Zhuang, Zhicheng Ji","doi":"10.1093/bib/bbag515","DOIUrl":"10.1093/bib/bbag515","url":null,"abstract":"<p><p>Spatial transcriptomics (ST) enables measurement of gene expression while preserving spatial organization within tissues. Accurate reconstruction of single-cell transcriptomes requires precise whole-cell segmentation, yet many ST experiments provide only nuclear staining images, making reliable inference of cell boundaries difficult. Here we introduce Halo, a pretrained segmentation model that reconstructs whole-cell boundaries by integrating nuclear morphology with the spatial distribution of RNA transcripts. Halo converts transcript coordinates into molecular density maps that are processed jointly with DAPI images using a Cellpose-SAM segmentation architecture. Halo is pretrained on multimodal Xenium data from 12 tissue types and can be directly applied to new datasets without additional training, providing a ready-to-use alternative to supervised approaches that require dataset-specific fitting. Across diverse tissues, Halo achieves substantially higher agreement with the 10$times$ multimodal reference segmentation than existing methods, in terms of both cell boundaries and RNA-to-cell assignments, while requiring only DAPI staining and transcript spatial information. Improved segmentation leads to more reliable cell-type identification and more accurate estimation of cell morphological features. By providing a pretrained, generalizable model for whole-cell reconstruction, Halo enables scalable and reproducible cell segmentation for image-based ST.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13584070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148988086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HESTIA: scalable multimodal integration of histology and high-resolution spatial Transcriptomics for robust spatial domain identification.","authors":"Zheng Zhong, Xiaoyu Zhu, Jing Guo, Sha Liao, Ao Chen","doi":"10.1093/bib/bbag512","DOIUrl":"10.1093/bib/bbag512","url":null,"abstract":"<p><p>Spatial omics has revolutionized molecular biology by providing invaluable insights into how native tissue microenvironments regulate cellular functions and disease mechanisms. Accurately capturing this structural complexity and decoding the underlying biological processes requires effectively integrating data from multiple modalities. However, transitioning to subcellular resolutions introduces massive data scales and severe transcriptomic sparsity, which challenge current analytical frameworks. To address this, we present HESTIA (Histology-Enhanced Scalable cross-Resolution inTegration for spatial trAnscriptomics), a highly efficient multimodal algorithm designed for identifying spatial domains in large-scale, high-resolution spatial omics data. By circumventing memory-intensive computations, HESTIA efficiently processes massive datasets on which existing algorithms fail due to memory constraints. HESTIA outperforms current multimodal methods in clustering accuracy and spatial continuity, accurately delineating fine structural boundaries. Furthermore, applying HESTIA to large-scale pathological samples successfully dissects clinically relevant intratumoral heterogeneity and maps distinct immune microenvironments in lung and colorectal cancers.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647271/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction of cross-immunity.","authors":"Vivien Erzsébet Resch, László Tóth, Anita Rácz, Dezső Virok, Gábor Paragi","doi":"10.1093/bib/bbag484","DOIUrl":"10.1093/bib/bbag484","url":null,"abstract":"<p><p>Cross-immunity, defined as the ability of T-cells to recognize multiple antigen peptide-major histocompatibility complexes, is a fundamental feature of adaptive immunity. However, the prediction of different peptide epitopes that can be recognized by the same T-cell receptor remains challenging. Currently, artificial intelligent (AI)-based machine learning (ML) methods can be successfully used for pattern recognition in epitope molecular space by detecting the functional similarity between peptide sequences. In this study, using literature-based experimental data, we examined ML-based binary classification models trained on small datasets to predict the activity of nine-amino-acid-long peptides. Our results suggest that the consensus function of well-established similarity matrix-based representations and structural-based descriptors of epitopes yields better performance because representation-specific noises are reduced and individual model weaknesses are partially compensated. We also sought to determine the extent to which the predictive power of the applied AIs procedure depended on the physicochemical content of the descriptor set during the training process. In addition, challenging the models, we applied them to an independent experimental dataset to examine the effects of diverse laboratory conditions on a regulated biological measurement. In summary, applying a consensus function can capture the biological complexity of cross-reactivity at the binary classification level, even when applied to relatively small datasets.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13561412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CAREPath: semantic context-aware reasoning paths with mechanism-augmented embeddings for drug repurposing.","authors":"Haerin Song, Dongmin Bang, Bonil Koo, Sun Kim, Sangseon Lee","doi":"10.1093/bib/bbag472","DOIUrl":"10.1093/bib/bbag472","url":null,"abstract":"<p><p>Biomedical knowledge graphs that include drugs, genes, and diseases support drug repurposing by connecting drugs to diseases through gene-mediated multi-hop paths, thereby enabling mechanism-of-action reasoning. However, deeper traversal does not necessarily improve mechanistic reasoning: long paths grow combinatorially and frequently pass through hub genes, producing irrelevant gene regulatory signals, whereas overly constrained or sparse paths may miss broader biological context. We propose Context-Aware REasoning Path (CAREPath), a knowledge graph (KG)-large language model framework inspired by depth-search and breadth-search reasoning to balance mechanistic specificity, scalability, and context recovery. The depth-search strategy constrains traversal to short disease-gene-drug paths, converts each path into a structured prompt, and encodes it with a biomedical language model to generate semantic path embeddings. Complementarily, the breadth-search strategy constructs entity-level mechanism-context embeddings from one-hop gene neighborhoods and enriches them through similarity-guided augmentation using pharmacologically related drugs and gene-signature-similar diseases. Across five biomedical KGs, CAREPath achieves the best area under the precision-recall curve (AUPRC) in the disease cold-start setting among 18 baselines, improving performance by up to 3.6%. Additional analyses show that semantic short-path encoding contributes most to performance, while mechanism-context augmentation improves robustness under sparse path signals and strengthens gene ontology functional agreement. Case studies and recently U.S. Food and Drug Administration (FDA)-approved indications further demonstrate its practical relevance, positioning CAREPath as a framework that supplies interpretable mechanistic rationales where constrained path is available, while remaining robust when it is not. Source code is available at https://github.com/hamppy-song/CAREPath.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.","authors":"Anthony Christidis, Andrew Ghazi, Smriti Chawla, Nitesh Turaga, Robert Gentleman, Ludwig Geistlinger","doi":"10.1093/bib/bbag496","DOIUrl":"10.1093/bib/bbag496","url":null,"abstract":"<p><p>Although cell type annotation has become an integral part of single-cell analysis workflows, the assessment of computational annotations remains challenging. Many annotation tools transfer labels from an annotated reference dataset to a new query dataset of interest, but blindly transferring labels from one dataset to another has its own set of challenges. Often enough there is no perfect alignment between datasets, especially when transferring annotations from a healthy reference atlas for the discovery of disease states. We present scDiagnostics, a new open-source software package that facilitates the detection of complex or ambiguous annotation cases that may otherwise go unnoticed, thus addressing a critical unmet need in current single-cell analysis workflows. scDiagnostics is equipped with novel diagnostic methods that are compatible with all major cell type annotation tools. We demonstrate that scDiagnostics reliably detects complex or conflicting annotations using both carefully designed simulated datasets and diverse real-world single-cell datasets. Our evaluation demonstrates that scDiagnostics reliably identifies misleading annotations that systematically distort downstream analysis and interpretation and that would otherwise remain undetected.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13548329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HighFold4: extending AlphaFold3 to accurate cyclic peptide conformation prediction via custom chemical connectivity.","authors":"Chengyun Zhang, Wentong Wang, Renjie Zhu, Sen Cao, Yiqi Xu, Ning Zhu, Yaling Wu, Jingjing Guo, Hongliang Duan","doi":"10.1093/bib/bbag505","DOIUrl":"10.1093/bib/bbag505","url":null,"abstract":"<p><p>While AlphaFold3 has revolutionized protein structure prediction and supports noncanonical amino acids, its architecture always fails to reliably generate the closed-ring topologies characteristic of cyclic peptides. Existing adaptations, such as imposing distance constraints via an offset matrix, enforce ring geometry but cannot specify the chemical identity of the cyclization bond, leading to a restrictive bias toward amide- or disulfide-linked macrocycles. Here, we present HighFold4, a framework that adapts AlphaFold3 to explicitly incorporate user-defined chemical connectivity between residues, thereby enabling both topological closure and bond-specific cyclization. Without retraining the base model, HighFold4 achieves accurate, chemistry-aware prediction of diverse cyclic peptide conformations, as validated on 179 structures. This work established a new paradigm for the conformation construction of macrocyclic peptides with tailored ring geometry and linkage chemistry, significantly expanding the utility of deep learning in peptide-based drug discovery.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13577668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148975787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PhoSARte: identification of SARS-CoV-2 phosphorylation sites using contrastive learning and protein language models.","authors":"Nhat Truong Pham, Duong Thanh Tran, Qiaosen Su, Yeona Jung, Nattanong Bupi, Dahyun Kang, Sukchan Lee, Balachandran Manavalan","doi":"10.1093/bib/bbag504","DOIUrl":"10.1093/bib/bbag504","url":null,"abstract":"<p><p>Phosphorylation, a critical post-translational modification, is extensively altered during viral infections, including SARS-CoV-2, where it plays a central role in modulating host-pathogen interactions. Accurately identifying these specific phosphorylation sites is crucial for understanding viral pathogenesis, prioritizing antiviral targets, guiding therapeutic strategies, and strengthening preparedness for future viral outbreaks. Although several computational tools have been proposed to complement experimental phosphoproteomics, existing methods often show limited robustness, cross-viral generalizability, and interpretability. To address these challenges, we developed PhoSARte, an interpretable computational framework that integrates Siamese network-based contrastive learning (SCL) with pretrained protein language models (PLMs) to accurately identify phosphorylation sites in SARS-CoV-2-infected cells. PhoSARte employs a unique dual-stream architecture: PLMs capture contextual protein sequence representations, while the SCL module, comprising a transformer-based encoder and an attention-based bidirectional gated recurrent unit, learns discriminative and similarity-preserving representations of protein sequence pairs using a contrastive loss function. The integration of these complementary representations substantially improves the robustness and generalizability of the framework. PhoSARte was rigorously benchmarked on phosphoproteomics datasets derived from infected A549 (Homo sapiens) and Vero E6 (Chlorocebus sabaeus) cells, as well as their combined dataset. Through rigorous cross-cell-type validation and testing, PhoSARte demonstrated superior performance, significantly outperforming current state-of-the-art methods. Importantly, an external cross-viral case study on entirely unseen adenovirus type 2-infected human IMR-90 cells confirmed the broad transferability of PhoSARte, demonstrating its capacity to generate actionable biological hypotheses under novel viral stress conditions. Furthermore, advancing beyond traditional black-box predictors, PhoSARte integrates an in silico mutagenesis analysis that decodes complex deep learning embeddings to successfully extract biologically relevant motif signatures. PhoSARte is freely accessible at https://balalab-skku.org/PhoSARte/, providing an accessible, interpretable, and adaptable framework for virus-associated phosphorylation site prediction, antiviral target prioritization, and host-directed therapeutic discovery.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"27 5","pages":""},"PeriodicalIF":7.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13577671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148975839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}