Briefings in bioinformatics最新文献

{"title":"GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.","authors":"Qizheng Wang, Fan Yang, Mengjie Fu, Yunyun Huo, Ju Wang","doi":"10.1093/bib/bbag470","DOIUrl":"10.1093/bib/bbag470","url":null,"abstract":"<p><p>Genetic interactions (GIs) underpin the functional connectivity of genes and pathways, and are important for dissecting genotype-phenotype relationships and identifying therapeutic targets for diseases. However, the scale of the human genome restricts systematic experimental interrogation of GIs. Existing computational tools focus on predicting synthetic lethality (SL) and synthetic viability (SV), the two primary forms of GIs, yet their accuracy and biological interpretability are compromised by inadequate modeling of the molecular mechanisms behind positive and negative interactions, as well as the limitation of negative samples. To overcome these challenges, we developed Genetic Interaction Graph Convolutional Network (GiGCN), a signed network modeling framework for the joint identification of gene pairs with SL and SV. We built a high-confidence signed genetic network by integrating verified GIs, and non-interacting gene pairs, together with gene semantic similarity derived from biological processes. By leveraging disentangled subspace decomposition, this framework separately models distinct functional dimensions within gene networks, enabling robust representation of context-dependent regulatory relationships and accurate discrimination of SL and SV events. Benchmark experiments demonstrate that GiGCN outperforms state-of-the-art approaches (area under receiver operating-characteristic curve: 0.978, and area under precision-recall curve: 0.944). Further analyses reveal biologically meaningful insights, including known and novel SL interactions centered on the oncogene MYC Proto-Oncogene (MYC), as well as SV interactions linked to autophagy and mitophagy pathways. This study provides a robust and interpretable network-based strategy for systematically exploring GIs. The GiGCN framework not only improves the precision of SL and SV prediction, but also offers mechanistic insights into gene functional relationships, thereby supporting the discovery of actionable therapeutic targets for cancer and other human diseases.</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/PMC13548324/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148900015","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":"Mapping the frontiers of genomic disease surveillance research in the ASEAN region: scientometric insights for precision public health and next-generation epidemic intelligence.","authors":"Jerico Bautista Ogaya, Rahmatullah Nazari, Olalekan John Okesanya, Andres Phillip Mayol, Emmanueal M Cabanig, Mohamed Mustaf Ahmed, Murtaza Jafari, Roel Nickelson M Solano, Joey Salinas Ramos, Zhinya Kawa Othman, Adriana Viola Miranda, Shuaibu Saidu Musa, Mae Frances G Sanico, Rolando A Alimen, Lin Xu, Junjie Huang, Thijs Kouwenhoven, Yusuff Adebayo Adebisi, Don Eliseo Lucero-Prisno","doi":"10.1093/bib/bbag573","DOIUrl":"10.1093/bib/bbag573","url":null,"abstract":"<p><p>Genomic disease surveillance has become a cornerstone of precision public health, yet the regional research architecture in Southeast Asia remains insufficiently characterized. This study provides a comprehensive scientometric mapping of genomic surveillance research in the Association of Southeast Asian Nations (ASEAN) region and identifies patterns relevant to the development of epidemic intelligence systems. A scientometric analysis of 8129 Scopus-indexed publications (2000-25) was conducted using Bibliometrix/Biblioshiny and VOSviewer software. Analyses included publication trends, citation patterns, collaboration networks, institutional productivity, funding sources, keyword co-occurrence, and thematic evolution. The field demonstrates robust annual growth (14.49%) and high international collaboration (43.92%). Thailand, led by Mahidol and Chulalongkorn Universities, emerged as the principal regional knowledge hub in terms of indexed scholarly visibility. Publication influence is concentrated in a limited set of high-impact journals (PLOS One, Emerging Infectious Diseases, Journal of Clinical Microbiology) and internationally connected author networks. Thematic evolution reveals a transition from molecular epidemiology toward whole-genome sequencing (WGS), antimicrobial resistance (AMR), zoonotic surveillance, and One Health intelligence. Growth modeling suggests sustained expansion through the early 2040s. Genomic disease surveillance research in Southeast Asia has grown substantially, with expanding international collaboration and thematic shifts toward WGS, AMR, zoonotic surveillance, and One Health. However, research output remains concentrated in a few countries and institutions, and bibliometric findings do not measure surveillance capacity or policy implementation. Further research integrating laboratory, funding, epidemiological, and policy evidence is needed across ASEAN.</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/PMC13671232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191154","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":"PEARL: novel protein targets recommendation model via heterogeneous graph representation learning.","authors":"Zixuan Liu, Jiasi Luan, Zhen Zhang, Kui Tang, Xingyu Qin, Zhixiao Qi, Borui Zhang, Jun Liao","doi":"10.1093/bib/bbag551","DOIUrl":"10.1093/bib/bbag551","url":null,"abstract":"<p><p>Peptide drug repurposing depends on identifying likely protein targets for a query peptide across a large candidate space, yet most existing peptide-protein interaction models are optimized for isolated pairwise classification rather than target prioritization. Here, we present PEARL, a recommendation-oriented heterogeneous graph learning framework for peptide target recommendation. PEARL represents peptides and proteins as distinct node types connected by known peptide-protein associations and multi-view peptide similarity, and combines graph-based representation learning with similarity-aware score refinement to rank candidate targets for a query peptide. On the UMPPI benchmark, PEARL achieved mean area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AUPR), and Matthews correlation coefficient values of 0.868, 0.855, and 0.571 in five-fold cross-validation, outperforming the strongest baseline, HGT-PepPI, by 4.4, 4.8, and 7.0 percentage points, respectively. Under clustered cold-start evaluation, PEARL delivered the best overall AUC across the novel-protein, novel-peptide, and novel-pair settings, indicating robust generalization under increasing sequence dissimilarity. PEARL also remained the top performer on two independent test sets, reaching an AUC of 0.863 on Test167 and 0.871 on Test1440. Additional analyses showed that PEARL can be effectively calibrated and that its learned latent space groups functionally related targets beyond simple sequence identity. In a case study of the unseen therapeutic peptide CIGB-814, PEARL ranked SPSB2 as the top candidate target. Docking and molecular dynamics simulations supported a stable binding mode, and surface plasmon resonance further confirmed direct binding with an apparent KD of 5.538 μM. Together, these results support PEARL as a scalable computational framework for peptide target recommendation and peptide drug repurposing.</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/PMC13647286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135207","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":"iProDNet: integrated probabilistic differential network inference under heterogeneous biological conditions.","authors":"Heewon Park, Seiya Imoto","doi":"10.1093/bib/bbag565","DOIUrl":"10.1093/bib/bbag565","url":null,"abstract":"<p><p>Understanding phenotype-specific network rewiring is essential for elucidating the molecular mechanisms underlying complex diseases. Although numerous differential network analysis methods have been developed, most focus on only a limited aspect of network information. Consequently, these approaches often fail to jointly characterize regulatory activity and network architecture, provide limited statistical evidence at the network level, and offer little capability for prioritizing key genes that drive phenotype-specific network rewiring. To address these limitations, we developed a novel computational framework, integrated probabilistic differential network analysis (iProDNet), for identifying phenotype-specific regulatory modules under heterogeneous biological conditions. A key feature of iProDNet is the integration of regulatory effects and network topological characteristics into a unified network-aware gene activity score. The framework further transforms gene-level differential activity into network-level statistical evidence using a nonparametric log-likelihood ratio approach and naturally prioritizes genes that contribute to phenotype-specific network rewiring. We evaluated iProDNet through Monte Carlo simulations and compared its performance with existing methods. Across diverse simulation settings, iProDNet consistently achieved superior discriminative performance while maintaining a balanced tradeoff between sensitivity and specificity. We further applied iProDNet to whole-blood RNA-sequencing data generated by the Japan Corona virus disease 2019 (COVID19) Task Force to identify molecular interactions associated with severe COVID19. The proposed framework identified distinct rewired subnetworks related to immune regulation, ribosome-mediated translation, and interferon-driven antiviral responses. Furthermore, iProDNet successfully prioritized biologically relevant markers, including B2M, ISG15, and multiple ribosomal protein genes, all of which have previously been implicated in COVID19 severity and antiviral immunity. These results demonstrate that iProDNet provides a statistically robust and biologically interpretable framework for identifying phenotype-specific regulatory modules and key regulatory drivers. As such, it offers new opportunities for investigating molecular rewiring mechanisms and discovering disease-associated therapeutic targets.</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/PMC13647284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135186","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":"Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.","authors":"Taiyu Zhu, Upamanyu Ghose, Héctor Climente-González, Joanna Howson, Sile Hu, Alejo Nevado-Holgado","doi":"10.1093/bib/bbag550","DOIUrl":"10.1093/bib/bbag550","url":null,"abstract":"<p><p>Cardiometabolic diseases (CMDs) are multifactorial disorders influenced by numerous genetic variants and their complex interactions. Although recent studies have advanced the understanding of genetic risk prediction, current approaches predominantly rely on linear models that may not fully capture the complex, nonlinear relationships between genetic factors. Here, we present DeepGP (Deep learning-based Genome-wide Predictor), a novel multimodal deep learning framework that incorporates bidirectional state space models to predict CMD risk using genome-wide variants and demographic data. We conducted extensive experiments to evaluate DeepGP's performance. First, in simulation studies incorporating joint genetic and environmental interactions, we demonstrated DeepGP's superior prediction performance across varying levels of heritability. When evaluated on eight CMDs in cohorts of European ancestry from the UK Biobank (UKB), DeepGP achieved significantly higher accuracy compared with conventional polygenic risk scores and machine learning methods. Model interpretability analysis identified both well-established genes and potential new signals contributing to the risk of the disease. Evaluation within UKB subpopulations of African and Caribbean ancestry provided preliminary evidence of within-resource transferability for type 2 diabetes. These results demonstrate the potential of deep learning technologies to enhance genetic risk stratification for complex diseases, while underscoring the need for external validation in more diverse cohorts.</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/PMC13647273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135166","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":"Explainable/interpretable artificial intelligence and knowledge graph neural network for synthetic lethality prediction: overview, challenges, and future directions.","authors":"Salsabil Felloussi, Fatima El Haoussi, Hamid Tairi, Noura Aherrahrou","doi":"10.1093/bib/bbag564","DOIUrl":"10.1093/bib/bbag564","url":null,"abstract":"<p><p>Synthetic lethality (SL) provides a powerful foundation for targeted oncology by identifying gene pairs whose simultaneous disruption is fatal to cancer cells while sparing healthy tissue. However, mapping these interactions experimentally across the human genome is restricted by massive combinatorial scale and high costs. To address this, computational approaches specifically graph neural networks (GNNs) have become the standard for modeling these complex biological relationships. Hence, by integrating heterogeneous knowledge graph (KG) and multi-omics data, GNNs can effectively capture the topological and molecular context required for accurate SL prediction. This review systematically evaluates the computational SL landscape across three core dimensions. First, we examine how diverse GNN architectures integrate multi-layered biological data. Second, we address model interpretability, contrasting intrinsically transparent architectures with post-hoc explanation methods, as a requirement for clinical trust. Finally, we explore the emerging role of Generative AI and large language models to leverage their zero-shot reasoning and broad biomedical knowledge.</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/PMC13657051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149164849","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":"DDTRN: predicting bacterial transcriptional regulatory networks based on gene sequences using dual descriptor.","authors":"Pu Nie, Bin-Guang Ma","doi":"10.1093/bib/bbag534","DOIUrl":"10.1093/bib/bbag534","url":null,"abstract":"<p><p>Accurate computational reconstruction of bacterial transcriptional regulatory network (TRN) remains a fundamental challenge in systems biology, particularly for non-model organisms lacking extensive transcriptomic data. We present DDTRN, a sequence-driven framework that formulates TRN inference as a binary classification task over concatenated regulator-target gene sequence pairs and employs a Dual Descriptor (DD) model to predict regulatory interactions. The DD architecture represents a sequence using two learnable components: Composition Weight Map (CWM) and Position Weight Function (PWF). We comprehensively evaluate DDTRN against six conventional machine learning (ML) baselines across eight benchmark bacterial datasets, including Escherichia coli (DREAM5, RegulonDB), Bacillus subtilis, Salmonella enterica, Corynebacterium glutamicum, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Streptomyces coelicolor. DDTRN achieves superior overall performance, attaining average area under the receiver operating characteristic curve and area under the precision-recall curve scores of 0.869 and 0.868, respectively, with particularly pronounced advantages at lower descriptor ranks where positional weighting compensates for limited sequence context. Systematic sensitivity analyses of rank, embedding dimension, and basis function count reveal stable optimal operating regimes, while subsampling experiments demonstrate strong robustness even with limited training data. Interpretability analyses show that PWF learns distinct periodic contributions across different rank granularities and that CWM preferentially weights meaningful k-mers. A case study on the E. coli dataset further illustrates that DDTRN identifies method-specific candidate targets complementary to those proposed by conventional approaches. By operating solely on genomic sequence, DDTRN provides a scalable, interpretable, and data-efficient framework for bacterial TRN inference in species where expression data are scarce, and it establishes a foundation for future multimodal integration with condition-specific regulatory information.</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/PMC13619341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149073586","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":"Dynamic perturbation analysis: a modular rewiring framework for decoding cancer stage progression and identifying therapeutic vulnerabilities.","authors":"Bin Yang, Mi Li, Xingyi Liu, Xiaojie Jin, Guang Hu","doi":"10.1093/bib/bbag572","DOIUrl":"10.1093/bib/bbag572","url":null,"abstract":"<p><p>Disease progression is driven not only by transcriptional alterations but also by dynamic reorganization of molecular interaction networks. However, most existing approaches focus on differential gene expression and fail to capture modular-level reorganization. Here, we present dynamic perturbation analysis (DPA), a modular rewiring framework that quantifies dynamic perturbations across disease stages using a novel metric termed the degree of perturbation. Using gastric cancer (GC) progression as a model system, DPA revealed that extensive modular rewiring persists despite limited stage-specific transcriptional changes after tumor initiation. DPA identified progression-associated perturbation programs characterized by extracellular matrix remodeling, oncogenic signaling activation, stemness acquisition, and late-stage microenvironmental adaptation. Perturbation analysis revealed two major disease-driving programs and prioritized previously unrecognized regulators, including protein tyrosine phosphatase non-receptor type 12 (PTPN12) and leucine rich repeat and Ig domain containing 1 (LINGO1), whose functional roles in GC progression were experimentally validated. Furthermore, modular rewiring-guided drug repurposing uncovered PJ-34 as a promising therapeutic candidate targeting late-stage disease-associated perturbations. Collectively, DPA provides a generalizable framework for decoding disease progression through dynamic modular rewiring, enabling the identification of driver programs, candidate regulators, and therapeutic vulnerabilities. This framework offers a scalable strategy for network-guided mechanistic discovery and therapeutic prioritization in complex diseases.</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/PMC13677786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219708","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":"Ribo-seq rRNA reads reveal co-predicted signatures of ribosome heterogeneity and collisions.","authors":"Edwin S Kyei-Baffour, William J Faller, Ferhat Alkan","doi":"10.1093/bib/bbag575","DOIUrl":"10.1093/bib/bbag575","url":null,"abstract":"<p><p>Ribosome heterogeneity is increasingly recognized as a key factor in various biological contexts, including development, cancer, and immune responses. To enable systematic exploration of this diversity, we previously developed a bioinformatics method that utilized rRNA reads from Ribo-seq data, detecting changes in ribosomal protein composition (dripARF) and ribosome collisions (dricARF) between conditions simply and efficiently. The method works by projecting differences in rRNA read abundance onto the 3D-structure of the ribosome, following which we perform enrichment tests on predefined heterogeneity-specific rRNA position sets. In this study, we apply this tool to publicly available Ribo-seq datasets to understand the impact of experimental protocol variations and to identify shared biological signals across studies. Our analyses show that the rRNA regions assigned to our prediction sets are consistently enriched in rRNA reads, supporting the core assumptions of our approach. We further examine how various protocol factors influence rRNA read distribution and implement a single-sample rRNA position set enrichment analysis-based weight to limit false positives. Finally, we highlight consistent ribosomal changes across these studies, including prominent co-predictors such as ribosome collisions, eS25, and uS19.</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/PMC13677787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219870","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":"Gene-Chronos: parameter-efficient developmental time inference using a pretrained single-cell foundation model.","authors":"Yinbo Liu, Handi Gao, Tian Tian","doi":"10.1093/bib/bbag469","DOIUrl":"10.1093/bib/bbag469","url":null,"abstract":"<p><p>Large-scale single-cell and spatial transcriptomic atlases enable the study of developmental processes at high resolution. However, most datasets capture only static snapshots of cells, making it difficult to infer continuous biological time from transcriptomic profiles. Existing temporal inference methods often show limited robustness across heterogeneous datasets, and recent single-cell foundation models, although powerful for representation learning, are not designed to capture continuous temporal relationships. We present Gene-Chronos, a parameter-efficient framework for developmental time inference built on a frozen pretrained Geneformer backbone. The model introduces learnable temporal prompt tokens and a temporal contrastive objective to extract time-informative signals and encourage temporally coherent organization of cell representations. Across multiple benchmark datasets spanning diverse species and developmental stages, Gene-Chronos outperforms existing approaches and demonstrates strong generalization to previously unseen samples. Attention-based analyses further identify genes associated with developmental progression, providing interpretable insights into temporal gene expression dynamics.</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/PMC13544629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148890928","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}