Briefings in bioinformatics最新文献_第4页

Machine learning methods for gene regulatory network inference. 基因调控网络推理的机器学习方法。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf470

Akshata Hegde, Tom Nguyen, Jianlin Cheng

引用次数: 0

Characterization of the complex TB pharmacogenomic landscape in Africa using bioinformatic tools. 利用生物信息学工具表征非洲复杂的结核病药物基因组学景观。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf484

Carola Oelofse, Anwani Siwada, Khaleila Flisher, Marlo Möller, Caitlin Uren

{"title":"Characterization of the complex TB pharmacogenomic landscape in Africa using bioinformatic tools.","authors":"Carola Oelofse, Anwani Siwada, Khaleila Flisher, Marlo Möller, Caitlin Uren","doi":"10.1093/bib/bbaf484","DOIUrl":"10.1093/bib/bbaf484","url":null,"abstract":"Currently, many of the world's most culturally and genetically diverse populations, located in Africa, risk exclusion from advancements in pharmacogenomics (PGx) and personalized medicine. Optimizing treatment outcomes for these populations is crucial, particularly for widespread diseases such as tuberculosis (TB). Reducing adverse drug reactions is essential for improving treatment adherence and overall outcomes. However, investigating the PGx landscape in African populations is challenging due to the lack of genotype and phenotype data, as well as limited computational tools and resources tailored to their genetic diversity. This study assessed various bioinformatic methodologies to characterize variations in the absorption, distribution, metabolism, and excretion (ADME) of anti-TB drugs in a large African cohort (>21 populations from public and in-house datasets). Special focus was placed on the Khoe-San, one of Africa's most genetically diverse groups, and the South African Coloured (SAC) community, whose richly diverse genetic background arises from recent admixture. We developed a graphic resource to support the investigation of anti-TB drug PGx in Africa. African-specific genomic studies addressing major health challenges on the continent are critical for informing the development of relevant genotyping and reference panels, enabling more cost-efficient personalized care in the region. This study offers a comprehensive assessment of the TB PGx landscape in Africa and highlights the potential of computational methods to promote the inclusion of genomically diverse African populations in PGx research.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12450348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145102504","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}

引用次数: 0

SageTCR: a structure-based model integrating residue- and atom-level representations for enhanced TCR-pMHC binding prediction. SageTCR：一个基于结构的模型，集成残基和原子级表示，用于增强TCR-pMHC结合预测。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf496

Xiangyi Li, Chuance Sun, Weiran Huang, Yanjing Wang, Buyong Ma

{"title":"SageTCR: a structure-based model integrating residue- and atom-level representations for enhanced TCR-pMHC binding prediction.","authors":"Xiangyi Li, Chuance Sun, Weiran Huang, Yanjing Wang, Buyong Ma","doi":"10.1093/bib/bbaf496","DOIUrl":"10.1093/bib/bbaf496","url":null,"abstract":"T-cell receptors (TCRs) recognize peptide-MHC (pMHC) complexes through intricate structural interactions, which is a core component of adaptive immunity. However, the diverse and cross-reactive nature of TCRs poses great challenges for accurate prediction of TCR-epitope interactions, hampering the advancement and broad application of TCR-related therapies. Here, we present SageTCR, a bi-level graph neural network (GNN) framework that leverages structural data to predict TCR-pMHC binding possibilities. Harnessing the pretrained language models, SageTCR encodes detailed structural arrangement at both residue-level and atomic-level and effectively integrates the bimodal representations via attention mechanisms. To tackle the deficiency of experimental structures, we explore comprehensive data augmentation strategies to enrich the training and increase the generalizability while concurrently preserving the characteristic TCR-pMHC diagonal binding mode. SageTCR demonstrates superior performance compared to six methods with different deep learning architectures. Furthermore, SageTCR offers the interpretability by identifying and focusing on the conformational features of pivotal contact residues on the interface, which can provide valuable insights for TCR engineering and immunotherapy design.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124166","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}

引用次数: 0

Benchmarking copy number variation detection with low-coverage whole-genome sequencing. 低覆盖率全基因组测序对标拷贝数变异检测。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf514

Nan Wang, Zi-Yu Tao, Tao Wu, Jinyu Wang, Weiliang Wang, Huaqiu Shi, Xue-Song Liu

{"title":"Benchmarking copy number variation detection with low-coverage whole-genome sequencing.","authors":"Nan Wang, Zi-Yu Tao, Tao Wu, Jinyu Wang, Weiliang Wang, Huaqiu Shi, Xue-Song Liu","doi":"10.1093/bib/bbaf514","DOIUrl":"10.1093/bib/bbaf514","url":null,"abstract":"Low-coverage whole-genome sequencing (lcWGS) provides a cost-effective method for genome-wide copy number variation (CNV) profiling, yet its technical limitations and analytical variability require systematic evaluation. We benchmarked five CNV detection tools using simulated and real-world datasets, focusing on sequencing depth, formalin-fixed paraffin-embedded (FFPE) artifacts, tumor purity, multi-center reproducibility, and signature-level stability. Our results demonstrate that ichorCNA outperformed other tools in precision and runtime at high purity (≥50%), making it the optimal choice for lcWGS-based workflows. Prolonged FFPE fixation induced artifactual short-segment CNVs due to formalin-driven DNA fragmentation, a bias none of the tools could computationally correct, necessitating strict fixation time control or prioritization of fresh-frozen samples. Multi-center analysis revealed high reproducibility for the same tool across sequencing facilities, but comparisons between different tools showed low concordance. Copy number features extracted by the Wang et al. method exhibited superior stability across conditions compared with the Steele et al. method and the Tao et al. method. This study establishes actionable guidelines for lcWGS: prioritize ichorCNA (ensuring ≥50% tumor purity), optimize FFPE protocol, and use Wang et al. features to ensure robust copy number profiling in precision oncology.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12481693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145198370","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}

引用次数: 0

Multi-omics time-series analysis in microbiome research: a systematic review. 微生物组研究中的多组学时间序列分析：系统综述。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf502

Moiz Khan Sherwani, Matti O Ruuskanen, Dylan Feldner-Busztin, Panos Nisantzis Firbas, Gergely Boza, Ágnes Móréh, Tuomas Borman, Pande Putu Erawijantari, István Scheuring, Shyam Gopalakrishnan, Leo Lahti

{"title":"Multi-omics time-series analysis in microbiome research: a systematic review.","authors":"Moiz Khan Sherwani, Matti O Ruuskanen, Dylan Feldner-Busztin, Panos Nisantzis Firbas, Gergely Boza, Ágnes Móréh, Tuomas Borman, Pande Putu Erawijantari, István Scheuring, Shyam Gopalakrishnan, Leo Lahti","doi":"10.1093/bib/bbaf502","DOIUrl":"https://doi.org/10.1093/bib/bbaf502","url":null,"abstract":"Recent developments in data generation have opened up unprecedented insights into living systems. It has been recognized that integrating and characterizing temporal variation simultaneously across multiple scales, from specific molecular interactions to entire ecosystems, is crucial for uncovering biological mechanisms and understanding the emergence of complex phenotypes. With the increasing number of studies incorporating multi-omics data sampled over time, it has become clear that integrated approaches are pivotal for these efforts. However, standard data analytical practices in longitudinal multi-omics are still shaping up and many of the available methods have not yet been widely evaluated and adopted. To address this gap, we performed the first systematic literature review that comprehensively categorizes, compares, and evaluates computational methods for longitudinal multi-omics integration, with a particular emphasis on four categories of the studies: (i) host and host-associated microbiome studies, (ii) microbiome-free host studies, (iii) host-free microbiome studies, and (iv) methodological framework studies. Our review highlights current methodological trends, identifies widely used and high-performing frameworks, and assesses each method across performance, interpretability, and ease of use. We further organize these methods into thematic groups-such as statistical modeling, machine learning, dimensionality reduction, and latent factor approaches-to provide a clear roadmap for future research and application. This work offers a critical foundation for advancing integrative longitudinal data science and supporting reproducible, scalable analysis in this rapidly evolving field.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145238116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AUPRC: a metric for evaluating the performance of in-silico perturbation methods in identifying differentially expressed genes. AUPRC：用于评估在识别差异表达基因的硅微扰方法的性能的度量。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf426

Hongxu Zhu, Amir Asiaee, Leila Azinfar, Jun Li, Han Liang, Ehsan Irajizad, Kim-Anh Do, James P Long

{"title":"AUPRC: a metric for evaluating the performance of in-silico perturbation methods in identifying differentially expressed genes.","authors":"Hongxu Zhu, Amir Asiaee, Leila Azinfar, Jun Li, Han Liang, Ehsan Irajizad, Kim-Anh Do, James P Long","doi":"10.1093/bib/bbaf426","DOIUrl":"10.1093/bib/bbaf426","url":null,"abstract":"In silico perturbation models, computational methods that can predict cellular responses to perturbations, present an opportunity to reduce the need for costly and time-intensive in vitro experiments. Many recently proposed models predict high-dimensional cellular responses, such as gene or protein expression to perturbations such as gene knockout or drugs. However, evaluating in silico performance has largely relied on metrics such as $R^{2}$, which assess overall prediction accuracy but fail to capture biologically significant outcomes like the identification of differentially expressed (DE) genes. In this study, we present a novel evaluation framework that introduces the AUPRC metric to assess the precision and recall of DE gene predictions. By applying this framework to both single-cell and pseudo-bulked datasets, we systematically benchmark simple and advanced computational models. Our results highlight a significant discrepancy between $R^{2}$ and AUPRC, with models achieving high $R^{2}$ values but struggling to identify DE genes, as reflected in their low AUPRC values. This finding underscores the limitations of traditional evaluation metrics and the importance of biologically relevant assessments. Our framework provides a more comprehensive understanding of model capabilities, advancing the application of computational approaches in cellular perturbation research.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12400816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144943463","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}

引用次数: 0

AttBiomarker: unveiling preeclampsia biomarkers and molecular pathways through two-stage gene selection techniques and attention-based CNN with gene regulatory network analysis. AttBiomarker：通过两阶段基因选择技术和基于注意力的CNN基因调控网络分析揭示子痫前期的生物标志物和分子途径。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf473

Sakib Sarker, S M Hasan Mahmud, Md Faruk Hosen, Kah Ong Michael Goh, Watshara Shoombuatong

{"title":"AttBiomarker: unveiling preeclampsia biomarkers and molecular pathways through two-stage gene selection techniques and attention-based CNN with gene regulatory network analysis.","authors":"Sakib Sarker, S M Hasan Mahmud, Md Faruk Hosen, Kah Ong Michael Goh, Watshara Shoombuatong","doi":"10.1093/bib/bbaf473","DOIUrl":"10.1093/bib/bbaf473","url":null,"abstract":"Preeclampsia is a complex pregnancy disorder that poses significant health risks to both mother and fetus. Despite its clinical importance, the underlying molecular mechanisms remain poorly understood. In this study, we developed an integrative deep learning and bioinformatics approach to identify potential biomarkers for preeclampsia. Three microarray datasets related to preeclampsia were initially analyzed to select a preliminary gene subset based on $P$-values. Feature selection was then performed in two consecutive rounds: first, the Fisher score method was applied to extract significant genes, followed by the minimum Redundancy Maximum Relevance method to refine the subset further. These selected gene subsets were trained using our proposed Attention-based Convolutional Neural Network (AttCNN), which achieved the highest classification accuracy compared with other models. From the experiments, a set of 58 common genes was identified between differentially expressed genes and the final optimized subset. Here, Gene Ontology and KEGG pathway enrichment analyses highlighted key biological processes and pathways associated with preeclampsia. Subsequently, a protein-protein interaction network was constructed, identifying 10 hub genes: TSC22D1, IRF3, MME, SRSF10, SOD1, HK2, ERO1L, SH3BP5, UBC, and ZFAND5. Further analysis of gene regulatory networks, including transcription factor-gene, gene-microRNA, and drug-gene interactions, revealed that seven hub genes (HK2, SRSF10, SOD1, ERO1L, IRF3, MME, and SH3BP5) were strongly associated with preeclampsia. Molecular docking analysis showed that HK2, SH3BP5, and SOD1 exhibited significant binding affinities with two preeclampsia drugs. These findings suggest that the identified hub genes hold promise as biomarkers for early prognosis, diagnosis, and potential therapeutic targets for preeclampsia.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145085115","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}

引用次数: 0

Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset. 基于全和部分多组学数据集的多层矩阵分解癌症亚型。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf448

Yingxuan Ren, Fengtao Ren, Bo Yang

{"title":"Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset.","authors":"Yingxuan Ren, Fengtao Ren, Bo Yang","doi":"10.1093/bib/bbaf448","DOIUrl":"10.1093/bib/bbaf448","url":null,"abstract":"Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering. MLMF initially processes multi-omics feature matrices by performing multi-layer linear or nonlinear factorization, decomposing the original data into latent feature representations unique to each omics type. These latent representations are subsequently fused into a consensus form, on which spectral clustering is performed to determine subtypes. Additionally, MLMF incorporates a class indicator matrix to handle missing omics data, creating a unified framework that can manage both complete and incomplete multi-omics data. Extensive experiments conducted on 12 multi-omics cancer datasets, both complete and with missing values, demonstrate that MLMF achieves results that are comparable to or surpass the performance of several state-of-the-art approaches. MLMF is open source and available at (https://github.com/renyingxuan/MLMF.git).","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12418959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145022873","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}

引用次数: 0

SLRanger: an integrated approach for spliced leader detection and operon prediction using long RNA reads. SLRanger：一种利用长RNA读取进行拼接先导检测和操纵子预测的综合方法。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf437

Yanwen Shao, Zhihao Guo, Jinpeng Chen, Runsheng Li

{"title":"SLRanger: an integrated approach for spliced leader detection and operon prediction using long RNA reads.","authors":"Yanwen Shao, Zhihao Guo, Jinpeng Chen, Runsheng Li","doi":"10.1093/bib/bbaf437","DOIUrl":"10.1093/bib/bbaf437","url":null,"abstract":"Spliced leader (SL) trans-splicing occurs in a wide range of eukaryotes and plays a critical role in processing mRNAs derived from operon structures. However, current research on this mechanism remains limited, partly due to the difficulty in accurately identifying genuine SL trans-splicing events. The advent of long-read RNA sequencing technologies, such as direct RNA sequencing by Oxford Nanopore Technologies, offers a more promising avenue for detecting these events with greater resolution. Here, we present SLRanger, an integrated tool to detect SL sequences and predict operon structures in eukaryotic transcriptomes. SLRanger improves upon the traditional Smith-Waterman (SW) alignment framework by incorporating an optimized scoring scheme tailored to SL detection in native long RNA reads. We primarily validated our method using direct RNA sequencing data from Caenorhabditis elegans, a well-established model organism for studying trans-splicing. Through a dynamic cutoff strategy, SLRanger robustly identified high-confidence SL-carrying reads. Leveraging the SL information, SLRanger achieved over 80% accuracy in operon gene prediction, recovering more than 70% of known operon genes in C. elegans. SLRanger was also applied to detect SL from cDNA long RNA reads and another trans-spliced species. Our results demonstrate that SLRanger not only provides a reliable approach for characterizing SL trans-splicing events but also serves as an effective framework for operon discovery, enabling transcriptomic analysis for operons and facilitating downstream data-mining applications.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12410068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144991297","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}

引用次数: 0

Contrastive hypergraph collaborative filtering for transfer RNA-disease association prediction. 转移rna -疾病关联预测的对比超图协同过滤。

IF 7.7 2区生物学

Briefings in bioinformatics Pub Date : 2025-08-31 DOI: 10.1093/bib/bbaf494

Tianxiang Ouyang, Yuanpeng Zhang, Zhijian Huang, Lei Deng

{"title":"Contrastive hypergraph collaborative filtering for transfer RNA-disease association prediction.","authors":"Tianxiang Ouyang, Yuanpeng Zhang, Zhijian Huang, Lei Deng","doi":"10.1093/bib/bbaf494","DOIUrl":"10.1093/bib/bbaf494","url":null,"abstract":"Transfer RNAs (tRNAs) play critical roles in the process of protein synthesis by decoding messenger RNA codons into amino acids, which is essential for cellular function across various biological pathways and for maintaining metabolic homeostasis. Available evidence implicates that tRNAs are involved in the progression of diverse diseases, underscoring the importance of accurately predicting tRNA-disease associations to understand disease mechanisms and support precision medicine. However, existing methods often struggle with the complexity and heterogeneity inherent in these associations. To address these challenges, we introduce contrastive hypergraph collaborative filtering (CoHGCL), a prediction framework that integrates hypergraph contrastive learning with collaborative filtering. CoHGCL employs graph attention networks to capture local structural features and random walk with restart algorithms to encode global topological patterns. Subsequently, a node-level contrastive learning mechanism alternates between standard graph and hypergraph representations to enhance multiview feature embeddings. These enriched representations are integrated by a collaborative filtering approach through the utilization of generalized matrix factorization for modeling linear associations and multilayer perceptrons for capturing nonlinear interactions. Extensive experimental results on five-fold cross-validation demonstrate that CoHGCL achieves superior performance compared to existing methods, with an area under the receiver operating characteristic curve of 0.9623, area under the precision-recall curve of 0.9430, outperforming all baselines across all metrics. Furthermore, case studies further confirm CoHGCL's effectiveness in discovering novel and biologically meaningful tRNA-disease associations. The source code and datasets are publicly available at https://github.com/Ouyang-cmd/CoHGCL.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 5","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136512","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}

引用次数: 0