Briefings in bioinformatics最新文献_第2页

VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data. VBayesMM：用于高维微生物组多组学数据重要关系排序的变分贝叶斯神经网络。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf300

Tung Dang, Artem Lysenko, Keith A Boroevich, Tatsuhiko Tsunoda

{"title":"VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data.","authors":"Tung Dang, Artem Lysenko, Keith A Boroevich, Tatsuhiko Tsunoda","doi":"10.1093/bib/bbaf300","DOIUrl":"10.1093/bib/bbaf300","url":null,"abstract":"The analysis of high-dimensional microbiome multiomics datasets is crucial for understanding the complex interactions between microbial communities and host physiological states across health and disease conditions. Despite their importance, current methods, such as the microbe-metabolite vectors approach, often face challenges in predicting metabolite abundances from microbial data and identifying keystone species. This arises from the vast dimensionality of metagenomics data, which complicates the inference of significant relationships, particularly the estimation of co-occurrence probabilities between microbes and metabolites. Here we propose the variational Bayesian microbiome multiomics (VBayesMM) approach, which aims to improve the prediction of metabolite abundances from microbial metagenomics data by incorporating a spike-and-slab prior within a Bayesian neural network. This allows VBayesMM to rapidly and precisely identify crucial microbial species, leading to more accurate estimations of co-occurrence probabilities between microbes and metabolites, while also robustly managing the uncertainty inherent in high-dimensional data. Moreover, we have implemented variational inference to address computational bottlenecks, enabling scalable analysis across extensive multiomics datasets. Our large-scale comparative evaluations demonstrate that VBayesMM not only outperforms existing methods in predicting metabolite abundances but also provides a scalable solution for analyzing massive datasets. VBayesMM enhances the interpretability of the Bayesian neural network by identifying a core set of influential microbial species, thus facilitating a deeper understanding of their probabilistic relationships with the host.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559194","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

Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence. 单序列双视图集成学习预测突变后蛋白质稳定性变化。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf319

Zhiwei Nie, Yiming Ma, Yutian Liu, Xiansong Huang, Zhihong Liu, Peng Yang, Fan Xu, Feng Yin, Zigang Li, Jie Fu, Zhixiang Ren, Wen-Bin Zhang, Jie Chen

{"title":"Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence.","authors":"Zhiwei Nie, Yiming Ma, Yutian Liu, Xiansong Huang, Zhihong Liu, Peng Yang, Fan Xu, Feng Yin, Zigang Li, Jie Fu, Zhixiang Ren, Wen-Bin Zhang, Jie Chen","doi":"10.1093/bib/bbaf319","DOIUrl":"10.1093/bib/bbaf319","url":null,"abstract":"Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability achieved state-of-the-art prediction performance on seven single-point mutation benchmark datasets, and comprehensively surpassed other methods on five of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607396","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

CMOMO: a deep multi-objective optimization framework for constrained molecular multi-property optimization. CMOMO：约束分子多属性优化的深度多目标优化框架。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf335

Xin Xia, Yajie Zhang, Xiangxiang Zeng, Xingyi Zhang, Chunhou Zheng, Yansen Su

{"title":"CMOMO: a deep multi-objective optimization framework for constrained molecular multi-property optimization.","authors":"Xin Xia, Yajie Zhang, Xiangxiang Zeng, Xingyi Zhang, Chunhou Zheng, Yansen Su","doi":"10.1093/bib/bbaf335","DOIUrl":"10.1093/bib/bbaf335","url":null,"abstract":"Molecular optimization, aiming to identify molecules with improved properties from a huge chemical search space, is a critical step in drug development. This task is challenging due to the need to optimize multiple properties while adhering to stringent drug-like criteria. Recently, numerous effective artificial intelligence methods have been proposed for molecular optimization. However, most of them neglect the constraints in molecular optimization, thereby limiting the development of high-quality molecules that simultaneously satisfy property objectives and constraint compliance. To address this issue, we proposed a deep multi-objective optimization framework, termed CMOMO, for constrained molecular multi-property optimization. The proposed CMOMO divides the optimization process into two stages, which enables it to use a dynamic constraint handling strategy to balance multi-property optimization and constraint satisfaction. Besides, a latent vector fragmentation based evolutionary reproduction strategy is designed to generate promising molecules effectively. Experimental results on two benchmark tasks show that the proposed CMOMO outperforms five state-of-the-art methods to obtain more successfully optimized molecules with multiple desired properties and satisfying drug-like constraints. Moreover, the superiority of CMOMO is verified on two practical tasks, including a potential protein-ligand optimization task of 4LDE protein, which is the structure of $beta $2-adrenoceptor GPCR receptor, and a potential inhibitor optimization task of glycogen synthase kinase-3$beta $ target (GSK3$beta $). Notably, CMOMO demonstrates a two-fold improvement in success rate for the GSK3$beta $ optimization task, successfully identifying molecules with favorable bioactivity, drug-likeness, synthetic accessibility, and adherence to structural constraints.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144599486","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

Spatial histology and gene-expression representation and generative learning via online self-distillation contrastive learning. 空间组织学和基因表达表征与在线自蒸馏对比学习的生成学习。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf317

Qianyi Yan, Xuan Li, Jiangnan Cui, Jianming Rong, Jingsong Zhang, Pingting Gao, Yaochen Xu, Fufang Qiu, Chunman Zuo

{"title":"Spatial histology and gene-expression representation and generative learning via online self-distillation contrastive learning.","authors":"Qianyi Yan, Xuan Li, Jiangnan Cui, Jianming Rong, Jingsong Zhang, Pingting Gao, Yaochen Xu, Fufang Qiu, Chunman Zuo","doi":"10.1093/bib/bbaf317","DOIUrl":"10.1093/bib/bbaf317","url":null,"abstract":"Spatial transcriptomics quantifies spatial molecular profiles alongside histology, enabling computational prediction of spatial gene expression distribution directly from whole slide images. Inspired by image-to-text alignment and generation, we introduce Magic, a self-training contrastive learning model designed for histology-to-gene expression prediction. Magic (i) employs contrastive learning to derive shared embeddings for histology and gene expression while utilizing a momentum-based module to generate pseudo-targets to reduce the impact of noise; and (ii) leverages a transformer-based decoder to predict the expression of 300 genes based on histological features. Trained on 75 760 spots from 56 breast cancer slices and validated on 11 026 spots from five independent slices, Magic outperforms existing methods in aligning and generating histology-gene expression data, achieving a 10% improvement over the second-best approach. Furthermore, Magic demonstrates robust generalization, effectively predicting gene expression in colorectal cancer samples and The Cancer Genome Atlas (TCGA) datasets through zero-shot learning. Notably, Magic's predicted gene expression captures interpatient differences, highlighting its strong potential for clinical applications.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574849","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

Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies. 推进边缘聚类和图嵌入生物网络分析：RASopathies的案例研究。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf320

Federico García-Criado, Pedro Seoane, Elena Rojano, Juan A G Ranea, James R Perkins

{"title":"Advancing edge-based clustering and graph embedding for biological network analysis: a case study in RASopathies.","authors":"Federico García-Criado, Pedro Seoane, Elena Rojano, Juan A G Ranea, James R Perkins","doi":"10.1093/bib/bbaf320","DOIUrl":"10.1093/bib/bbaf320","url":null,"abstract":"Understanding and predicting biological processes from protein-protein interaction (PPI) networks requires accurate and efficient representations of their structure. However, many existing methods fail to capture the complex, overlapping modular structure of biological systems. To address this, we propose a network embedding strategy that improves both biological interpretability and predictive power. By transforming networks into a low-dimensional space while preserving key topological properties, embedding enables the discovery of novel functional relationships. Pre-clustering a network before embedding enhances representation quality, i.e. the ability to preserve meaningful structural and functional properties in the embedding space. However, traditional non-overlapping clustering methods can introduce bias by ignoring the overlapping nature of biological communities. We overcome this limitation by integrating the Hierarchical Link Clustering (HLC) algorithm into an embedding workflow tailored for large, weighted, undirected networks. First, we introduce two optimized HLC implementations for Python and R, both outperforming existing methods in clustering accuracy and scalability. Then, by restricting random walks to HLC-defined communities, we improve the representation of biological pathways, as shown using Reactome on the human PPI network. We also apply our full cluster embedding workflow to analyze RASopathies, a group of interrelated disorders with a diverse range of phenotypes, caused by mutations in genes from the RAS/MAPK pathway. This approach was used not only to represent known pathways, but also to identify potential novel gene candidates associated with RASopathies, including Noonan and Costello syndrome. HLC implementations are available in the CDLIB library (https://github.com/GiulioRossetti/cdlib), and at https://github.com/jimrperkins/linkcomm for Python and R, respectively.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574839","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

Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions. 综合系统生物学方法分析微生物群落失调和物种相互作用。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf323

Syed Sabih Ur Rehman, Muhammad Ibtisam Nasar, Cristina S Mesquita, Souhaila Al Khodor, Richard A Notebaart, Sascha Ott, Sunil Mundra, Ramesh P Arasardanam, Khalid Muhammad, Mohammad Tauqeer Alam

{"title":"Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions.","authors":"Syed Sabih Ur Rehman, Muhammad Ibtisam Nasar, Cristina S Mesquita, Souhaila Al Khodor, Richard A Notebaart, Sascha Ott, Sunil Mundra, Ramesh P Arasardanam, Khalid Muhammad, Mohammad Tauqeer Alam","doi":"10.1093/bib/bbaf323","DOIUrl":"10.1093/bib/bbaf323","url":null,"abstract":"Microbiomes are crucial for human health and well-being, with microbial dysbiosis being linked to various complex diseases. Therefore, understanding the structural and functional changes in the microbiome, along with the underlying mechanisms in disease conditions, is essential. In this review, we outline the structure and function of different human microbiomes and examine how changes in their composition may contribute to diseases. We highlight critical information associated with microbial dysbiosis and explore various therapeutic strategies for restoring a healthy microbiome, including microbiota transplantation, phage therapy, probiotics, prebiotics, dietary interventions, and drug-based approaches. Further, to better understand microbiome dysbiosis, we discuss multi-omics approaches including metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics, alongside computational modeling approaches such as ecological and metabolic network analysis. We outline key challenges associated with multi-omics techniques and emphasize the importance of integrative systems biology approaches that combine multi-omics data with computational modeling. These approaches are crucial for effectively analyzing microbiome data, providing deeper insights into species interactions and microbiome dynamics. Finally, we offer insights into future research directions in the field of microbiome research. This review makes a unique contribution to microbiome research by presenting a holistic framework that integrates multi-omics data with multi-scale modeling to elucidate microbial interactions, microbiome dysbiosis, and their modulation in disease-associated contexts.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229991/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574844","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

Advancing genetic engineering with active learning: theory, implementations and potential opportunities. 主动学习推进基因工程：理论、实施和潜在机会。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf286

Qixiu Du, Haochen Wang, Benben Jiang, Xiaowo Wang

引用次数: 0

Genomic language models (gLMs) decode bacterial genomes for improved gene prediction and translation initiation site identification. 基因组语言模型（gLMs）解码细菌基因组，以改进基因预测和翻译起始位点鉴定。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf311

Genereux Akotenou, Achraf El Allali

{"title":"Genomic language models (gLMs) decode bacterial genomes for improved gene prediction and translation initiation site identification.","authors":"Genereux Akotenou, Achraf El Allali","doi":"10.1093/bib/bbaf311","DOIUrl":"10.1093/bib/bbaf311","url":null,"abstract":"Accurate bacterial gene prediction is essential for understanding microbial functions and advancing biotechnology. Traditional methods based on sequence homology and statistical models often struggle with complex genetic variations and novel sequences due to their limited ability to interpret the \"language of genes.\" To overcome these challenges, we explore genomic language models (gLMs)-inspired by large language models in natural language processing-to enhance bacterial gene prediction. These models learn patterns and contextual dependencies within genetic sequences, similar to how LLMs process human language. We employ transformers, specifically DNABERT, for bacterial gene prediction using a two-stage framework: first, identifying coding sequence (CDS) regions, and then refining predictions by identifying the correct translation initiation sites (TIS). DNABERT is fine-tuned on a curated set of NCBI complete bacterial genomes using a k-mer tokenizer for sequence processing. Our results show that GeneLM significantly improves gene prediction accuracy. Compared with the leading prokaryotic gene finders, Prodigal, GeneMark-HMM, and Glimmer, and other recent deep learning methods, GeneLM reduces missed CDS predictions while increasing matched annotations. More notably, our TIS predictions surpass traditional methods when tested against experimentally verified sites. GeneLM demonstrates the power of gLMs in decoding genetic information, achieving state-of-the-art performance in bacterial genome analysis. This advancement highlights the potential of language models to revolutionize genome annotation, outperforming conventional tools and enabling more precise genetic insights.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12222049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552340","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

spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration. spaLLM：通过大语言模型集成增强多组学数据的空间域分析。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf304

Longyi Li, Liyan Dong, Hao Zhang, Dong Xu, Yongli Li

{"title":"spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration.","authors":"Longyi Li, Liyan Dong, Hao Zhang, Dong Xu, Yongli Li","doi":"10.1093/bib/bbaf304","DOIUrl":"10.1093/bib/bbaf304","url":null,"abstract":"Spatial multi-omics technologies provide valuable data on gene expression from various omics in the same tissue section while preserving spatial information. However, deciphering spatial domains within spatial omics data remains challenging due to the sparse gene expression. We propose spaLLM, the first multi-omics spatial domain analysis method that integrates large language models to enhance data representation. Our method combines a pre-trained single-cell language model (scGPT) with graph neural networks and multi-view attention mechanisms to compensate for limited gene expression information in spatial omics while improving sensitivity and resolution within modalities. SpaLLM processes multiple spatial modalities, including RNA, chromatin, and protein data, potentially adapting to emerging technologies and accommodating additional modalities. Benchmarking against eight state-of-the-art methods across four different datasets and platforms demonstrates that our model consistently outperforms other advanced methods across multiple supervised evaluation metrics. The source code for spaLLM is freely available at https://github.com/liiilongyi/spaLLM.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552342","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

Gene regulatory network integration with multi-omics data enhances survival predictions in cancer. 基因调控网络与多组学数据的整合提高了癌症患者的生存预测。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-07-02 DOI: 10.1093/bib/bbaf315

Romana T Pop, Ping-Han Hsieh, Tatiana Belova, Anthony Mathelier, Marieke L Kuijjer

{"title":"Gene regulatory network integration with multi-omics data enhances survival predictions in cancer.","authors":"Romana T Pop, Ping-Han Hsieh, Tatiana Belova, Anthony Mathelier, Marieke L Kuijjer","doi":"10.1093/bib/bbaf315","DOIUrl":"10.1093/bib/bbaf315","url":null,"abstract":"The emergence of high-throughput omics technologies has resulted in their wide application to cancer studies, greatly increasing our understanding of the disruptions occurring at different molecular levels. To fully harness these data, integrative approaches have emerged as essential tools, enabling the combination of multiple omics modalities to uncover disease mechanisms. However, many such approaches overlook gene regulatory mechanisms, which play a central role in the development and progression of cancer. Patient-specific gene regulatory networks (GRNs), representing interactions between regulators (such as transcription factors) and their target genes in each individual tumour, offer a powerful framework to bridge this gap and investigate the regulatory landscape of cancer. In this study, we introduce a novel approach for integrating patient-specific GRNs with multi-omic data and assess whether their inclusion in joint dimensionality reduction models improves survival prediction across multiple cancer types. By applying our method on ten cancer datasets from The Cancer Genome Atlas, we demonstrate that incorporating GRNs enhances associations with patient survival in several cancer types. Focusing on liver cancer, with validation in independent data, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression. These were linked to dysregulated fatty acid metabolism, and identified JUND as a potential novel transcriptional regulator driving these processes. Our findings highlight the value of network-based multi-omics integration for uncovering clinically relevant regulatory mechanisms and improving our understanding of cancer biology at the patient-specific level.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574841","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