Briefings in bioinformatics最新文献_第5页

Decoding herbal combination models through systematic strategies: insights from target information and traditional Chinese medicine clinical theory. 通过系统策略解读中药组合模型：来自靶点信息和中医临床理论的见解。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf229

Mingjuan Wang, Xuetong Chen, Mingxing Liu, Huiying Luo, Shuangshuang Zhang, Jie Guo, Jinghui Wang, Li Zhou, Na Zhang, Hongyan Li, Chao Wang, Liang Li, Zhenzhong Wang, Haiqing Wang, Zihu Guo, Yan Li, Yonghua Wang

{"title":"Decoding herbal combination models through systematic strategies: insights from target information and traditional Chinese medicine clinical theory.","authors":"Mingjuan Wang, Xuetong Chen, Mingxing Liu, Huiying Luo, Shuangshuang Zhang, Jie Guo, Jinghui Wang, Li Zhou, Na Zhang, Hongyan Li, Chao Wang, Liang Li, Zhenzhong Wang, Haiqing Wang, Zihu Guo, Yan Li, Yonghua Wang","doi":"10.1093/bib/bbaf229","DOIUrl":"10.1093/bib/bbaf229","url":null,"abstract":"Traditional Chinese medicine (TCM) utilizes intricate herbal formulations that exemplify the principles of compatibility and synergy. However, the rapid proliferation of herbal data has resulted in redundant information, complicating the understanding of their potential mechanisms. To address this issue, we first established a comprehensive database that encompasses 992 herbs, 18 681 molecules, and 2168 targets. Consequently, we implemented a multi-network strategy based on a core information screening method to elucidate the highly intertwined relationships among the targets of various herbs and to refine herbal target information. Within a non-redundant network framework, separation and overlap analysis demonstrated that the networking of herbs preserves essential clinical information, including their properties, meridians, and therapeutic classifications. Furthermore, two notable trends emerged from the statistical analyses of classical TCM formulas: the separation of herbs and the overlap between herbs and diseases. This phenomenon is termed the herbal combination model (HCM), validated through statistical analyses of two representative case studies: the common cold and rheumatoid arthritis. Additionally, in vivo and in vitro experiments with the new formula YanChuanQin (YanHuSuo-Corydalis Rhizoma, ChuanWu-Aconiti Radix, and QinJiao-Gentianae Macrophyllae Radix) for acute gouty arthritis further support the HCM. Overall, this computational method provides a systematic network strategy for exploring herbal combinations in complex and poorly understood diseases from a non-redundant perspective.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12100621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126697","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

KansformerEPI: a deep learning framework integrating KAN and transformer for predicting enhancer-promoter interactions. KansformerEPI：一个集成KAN和transformer的深度学习框架，用于预测增强子-启动子相互作用。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf272

Tianjiao Zhang, Saihong Shao, Hongfei Zhang, Zhongqian Zhao, Xingjie Zhao, Xiang Zhang, Zhenxing Wang, Guohua Wang

{"title":"KansformerEPI: a deep learning framework integrating KAN and transformer for predicting enhancer-promoter interactions.","authors":"Tianjiao Zhang, Saihong Shao, Hongfei Zhang, Zhongqian Zhao, Xingjie Zhao, Xiang Zhang, Zhenxing Wang, Guohua Wang","doi":"10.1093/bib/bbaf272","DOIUrl":"10.1093/bib/bbaf272","url":null,"abstract":"Enhancer-promoter interaction (EPI) is a critical component of gene regulation. Accurately predicting EPIs across diverse cell types can advance our understanding of the molecular mechanisms behind transcriptional regulation and provide valuable insights into the onset and progression of related diseases. At present, large-scale genome-wide EPI predictions typically rely on computational approaches. However, most of these methods focus on predicting EPIs within a single cell line and lack a global perspective encompassing multiple cell lines. Furthermore, they often fail to fully account for the nonlinear relationships between features, leading to suboptimal prediction accuracy. In this study, we propose KansformerEPI, a global EPI prediction model designed for multiple cell lines. The model is built on Kansformer, an encoder that integrates KAN and Transformer, effectively capturing the nonlinear relationships among various epigenetic and sequence features. We utilized KansformerEPI to achieve cross-tissue prediction of EPIs across different cell types. This approach enhances the model's scalability, eliminating the complexity of designing separate prediction models for individual tissues. As a result, our model is applicable to various tissues, thereby reducing dependency on extensive datasets. Experimental results demonstrate that KansformerEPI surpasses existing methods such as TransEPI, TargetFinder, and SPEID in both accuracy and stability of EPI predictions across datasets including HMEC, IMR90, K562, and NHEK.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293340","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

VirulentHunter: deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts. VirulentHunter：基于深度学习的毒力因子预测器阐明了不同微生物环境下的致病性。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf271

Chen Chen, Yong Xu, Jian Ouyang, Xiangyi Xiong, Paweł P Łabaj, Agnieszka Chmielarczyk, Anna Różańska, Hao Zhang, Keyang Liu, Tieliu Shi, Jun Wu

{"title":"VirulentHunter: deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts.","authors":"Chen Chen, Yong Xu, Jian Ouyang, Xiangyi Xiong, Paweł P Łabaj, Agnieszka Chmielarczyk, Anna Różańska, Hao Zhang, Keyang Liu, Tieliu Shi, Jun Wu","doi":"10.1093/bib/bbaf271","DOIUrl":"10.1093/bib/bbaf271","url":null,"abstract":"Virulence factors (VFs) are critical determinants of bacterial pathogenicity, but current homology-based identification methods often miss novel or divergent VFs, and many machine learning approaches neglect functional classification. Here, we present VirulentHunter, a novel deep learning framework that enable simultaneous VF identification and classification directly from protein sequences by leveraging the crucial step of fine-tuning pretrained protein language model. We curate a comprehensive VF database by integrating diverse public resources and expanding VF category annotations. Our benchmarking results demonstrate that VirulentHunter outperforms existing methods, particularly in identifying VFs lacking detectable homologs. Additionally, strain-level analysis using VirulentHunter highlights distinct pathogenicity profiles between Mycobacterium tuberculosis and Mycobacterium avium, revealing enrichment in VFs related to adherence, effector delivery systems, and immune modulation in M. tuberculosis, compared to biofilm formation and motility in M. avium. Furthermore, metagenomic profiling of gut microbiota from inflammatory bowel disease patient reveals a depletion of VFs associated with immune homeostasis. These results underscore the versatility of VirulentHunter as a powerful tool for VF analysis across diverse applications. To facilitate broader accessibility, we provide a freely accessible web service for VF prediction (http://www.unimd.org/VirulentHunter), accommodating protein sequences, genomes, and metagenomic data.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12167765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144301155","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

Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction. 集成多模态层次融合和元学习增强分子性质预测。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf251

Xianjun Han, Zhenglong Zhang, Can Bai, Zijian Wu

{"title":"Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction.","authors":"Xianjun Han, Zhenglong Zhang, Can Bai, Zijian Wu","doi":"10.1093/bib/bbaf251","DOIUrl":"10.1093/bib/bbaf251","url":null,"abstract":"Accurately predicting the pharmacological and toxicological properties of molecules is a critical step in the drug development process. Owing to the heterogeneity of molecular property prediction tasks, most of the current methods rely on building a base model and fine-tuning it to address specific properties. However, constructing a high-quality base model is a time-consuming procedure and requires a carefully designed network architecture; in addition, in certain rare molecular property prediction tasks, the base model often does not transfer well to new tasks. In this work, we adopt a meta-learning-based training framework that enables our model to adapt to diverse tasks with limited data, thereby preventing data scarcity from impacting certain molecular property predictions. Additionally, this framework leverages the correlations between different tasks, allowing the constructed model to quickly adapt to new prediction tasks. Moreover, we propose a multimodal fusion framework that combines two-dimensional molecular graphs with molecular images. In the molecular graphs, node-, motif-, and graph-level features are hierarchically guided from low to high levels, fully exploiting the molecular representation and more efficiently conducting hierarchical fusion. Experimental results indicate that our model outperforms the baseline models across various performance indicators, thereby validating the effectiveness of our approach.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12123521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186601","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

Artificial intelligence-driven circRNA vaccine development: multimodal collaborative optimization and a new paradigm for biomedical applications. 人工智能驱动的环状rna疫苗开发：多模式协同优化和生物医学应用的新范式。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf263

Yan Zhao, Huaiyu Wang

{"title":"Artificial intelligence-driven circRNA vaccine development: multimodal collaborative optimization and a new paradigm for biomedical applications.","authors":"Yan Zhao, Huaiyu Wang","doi":"10.1093/bib/bbaf263","DOIUrl":"10.1093/bib/bbaf263","url":null,"abstract":"Circular RNA (circRNA) vaccines have emerged as a groundbreaking innovation in infectious disease prevention and cancer immunotherapy, offering superior stability and reduced immunogenicity compared to conventional linear messenger RNA (mRNA) vaccines. While linear mRNA vaccines are prone to degradation and can trigger strong innate immune responses, covalently closed circRNA vaccines leverage their unique circular structure to enhance molecular stability and minimize innate immune activation, positioning them as a next-generation platform for vaccine development. Artificial intelligence (AI) is revolutionizing circRNA vaccine design and optimization. Deep learning models, such as convolutional neural networks (CNNs) and Transformers, integrate multi-omics data to refine antigen prediction, RNA secondary structure modeling, and lipid nanoparticle delivery system formulation, surpassing traditional bioinformatics approaches in both accuracy and efficiency. While AI-driven bioinformatics enhances antigen screening and delivery system modeling, generative AI accelerates literature synthesis and experimental planning-though the risk of fabricated references and limited biological interpretability hinders its reliability. Despite these advancements, challenges such as the \"black-box\" nature of AI algorithms, unreliable literature retrieval, and insufficient integration of biological mechanisms underscore the necessity for a hybrid \"AI-traditional-experimental\" paradigm. This approach integrates explainable AI frameworks, multi-omics validation, and ethical oversight to ensure clinical translatability. Future research should prioritize mechanism-driven AI models, real-time experimental feedback, and rigorous ethical standards to fully unlock the potential of circRNA vaccines in precision oncology and global health.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246484","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

Systematic identification of cancer-type-specific drugs based on essential genes and validations in lung adenocarcinoma. 基于必要基因的癌症类型特异性药物的系统鉴定及其在肺腺癌中的验证。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf266

Xiang Lian, Xia Kuang, Dong-Dong Zhang, Qian Xu, Anqiang Ye, Cheng-Yu Wang, Hong-Tu Cui, Hai-Xia Guo, Ji-Yun Zhang, Yuan Liu, Ge-Fei Hao, Zhenshun Cheng, Feng-Biao Guo

{"title":"Systematic identification of cancer-type-specific drugs based on essential genes and validations in lung adenocarcinoma.","authors":"Xiang Lian, Xia Kuang, Dong-Dong Zhang, Qian Xu, Anqiang Ye, Cheng-Yu Wang, Hong-Tu Cui, Hai-Xia Guo, Ji-Yun Zhang, Yuan Liu, Ge-Fei Hao, Zhenshun Cheng, Feng-Biao Guo","doi":"10.1093/bib/bbaf266","DOIUrl":"10.1093/bib/bbaf266","url":null,"abstract":"Depicting a global landscape of essential gene-targeting drugs would provide more opportunities for cancer therapy. However, a systematic investigation on drugs targeting essential genes still has not been reported. We suppose that drugs targeting cancer-type-specific essential genes would generally have less toxicity than those targeting pan-cancer essential genes. A scoring function-based strategy was developed to identify cancer-type-specific targets and drugs. The EssentialitySpecificityScore ranked the essential genes in 19 cancer types, and 1151 top genes were identified as cancer-type-specific targets. Combining target-drug interaction databases with research/marketing status, 370 cancer-type-specific drugs were identified, bound to 100 out of all identified targets. Profiles of applied cancer types of identified targets and drugs illustrate the scoring strategy's effectiveness: most drugs apply to cancer types <10. Seven drugs with no previous anticancer evidence were validated in 11 lung adenocarcinoma cell lines, and lower inhibition rates (from 9.4% to 44.0%) were observed in 10 normal cell lines. This difference is statistically significant (Student's t-test, P ≤ .0001), confirming the rationality of our supposition. Our built EGKG (Essential Gene Knowledge Graph) forms a computational basis to uncover essential gene targets and drugs for specific cancer types. It is available at http://gepa.org.cn/egkg/. Also, our experimental result suggests that combining drugs with orthogonal essentiality may be an alternative way to improve anticancer effects while maintaining biocompatibility. The code and data are available at https://github.com/KKINGA1/EGKG_data_process.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12145228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246528","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

EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species. EDS-Kcr：基于大语言模型的深度监督，用于识别多个物种的蛋白质赖氨酸巴豆酰化位点。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf249

Hong-Qi Zhang, Xin-Ran Lin, Yan-Ting Wang, Wen-Fang Pei, Guang-Ji Ma, Ze-Xu Zhou, Ke-Jun Deng, Dan Yan, Tian-Yuan Liu

{"title":"EDS-Kcr: deep supervision based on large language model for identifying protein lysine crotonylation sites across multiple species.","authors":"Hong-Qi Zhang, Xin-Ran Lin, Yan-Ting Wang, Wen-Fang Pei, Guang-Ji Ma, Ze-Xu Zhou, Ke-Jun Deng, Dan Yan, Tian-Yuan Liu","doi":"10.1093/bib/bbaf249","DOIUrl":"10.1093/bib/bbaf249","url":null,"abstract":"With the rapid advancement of proteomics, post-translational modifications, particularly lysine crotonylation (Kcr), have gained significant attention in basic research, drug development, and disease treatment. However, current methods for identifying these modifications are often complex, costly, and time-consuming. To address these challenges, we have proposed EDS-Kcr, a novel bioinformatics tool that integrates the state-of-the-art protein language model ESM2 with deep supervision to improve the efficiency and accuracy of Kcr site prediction. EDS-Kcr demonstrated outstanding performance across various species datasets, proving its applicability to a wide range of proteins, including those from humans, plants, animals, and microbes. Compared to existing Kcr site prediction models, our model excelled in multiple key performance indicators, showcasing superior predictive power and robustness. Furthermore, we enhanced the transparency and interpretability of EDS-Kcr through visualization techniques and attention mechanisms. In conclusion, the EDS-Kcr model provides an efficient and reliable predictive tool suitable for disease diagnosis and drug development. We have also established a freely accessible web server for EDS-Kcr at http://eds-kcr.lin-group.cn/.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198279","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

mKmer: an unbiased K-mer embedding of microbiomic single-microbe RNA sequencing data. mKmer：一种无偏K-mer包埋微生物组单微生物RNA测序数据。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf227

Fangyu Mo, Qinghong Qian, Xiaolin Lu, Dihuai Zheng, Wenjie Cai, Jie Yao, Hongyu Chen, Yujie Huang, Xiang Zhang, Sanling Wu, Yifei Shen, Yinqi Bai, Yongcheng Wang, Weiqin Jiang, Longjiang Fan

{"title":"mKmer: an unbiased K-mer embedding of microbiomic single-microbe RNA sequencing data.","authors":"Fangyu Mo, Qinghong Qian, Xiaolin Lu, Dihuai Zheng, Wenjie Cai, Jie Yao, Hongyu Chen, Yujie Huang, Xiang Zhang, Sanling Wu, Yifei Shen, Yinqi Bai, Yongcheng Wang, Weiqin Jiang, Longjiang Fan","doi":"10.1093/bib/bbaf227","DOIUrl":"10.1093/bib/bbaf227","url":null,"abstract":"The advanced single-microbe RNA sequencing (smRNA-seq) technique addresses the pressing need to understand the complexity and diversity of microbial communities, as well as the distinct microbial states defined by different gene expression profiles. Current analyses of smRNA-seq data heavily rely on the integrity of reference genomes within the queried microbiota. However, establishing a comprehensive collection of microbial reference genomes or gene sets remains a significant challenge for most real-world microbial ecosystems. Here, we developed an unbiased embedding algorithm utilizing K-mer signatures, named mKmer, which bypasses gene or genome alignment to enable species identification for individual microbes and downstream functional enrichment analysis. By substituting gene features in the canonical cell-by-gene matrix with highly conserved K-mers, we demonstrate that mKmer outperforms gene-based methods in clustering and motif inference tasks using benchmark datasets from crop soil and human gut microbiomes. Our method provides a reference genome-free analytical framework for advancing smRNA-seq studies.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12100620/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126698","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 Swin transformer: new deep learning method for colorectal cancer prognosis using transcriptomic data. Gene Swin transformer：利用转录组学数据预测结直肠癌预后的新深度学习方法。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf275

Yangyang Wang, Xinyu Yue, Shenghan Lou, Peinan Feng, Binbin Cui, Yanlong Liu

{"title":"Gene Swin transformer: new deep learning method for colorectal cancer prognosis using transcriptomic data.","authors":"Yangyang Wang, Xinyu Yue, Shenghan Lou, Peinan Feng, Binbin Cui, Yanlong Liu","doi":"10.1093/bib/bbaf275","DOIUrl":"10.1093/bib/bbaf275","url":null,"abstract":"Transcriptome sequencing has become essential in clinical tumor research, providing in-depth insights into the biology and functionality of tumor cells. However, the vast amount of data generated and the complex relationships between gene expressions make it challenging to effectively identify clinically relevant information. In this study, we developed a method called Gene Swin Transformer to address these challenges. This approach converts transcriptomic data into Synthetic Image Elements (SIEs). We utilized data from 12 datasets, including GSE17536-GSE103479 datasets (n = 1771) and The Cancer Genome Atlas (n = 459), to generate SIEs. These elements were then classified based on survival time using deep learning algorithms to predict colorectal cancer prognosis and build a reliable prognostic model. We trained and evaluated four deep learning models-BeiT, ResNet, Swin Transformer, and ViT Transformer-and compared their performance. The enhanced Swin-T model outperformed the other models, achieving weighted precision, recall, and F1 scores of 0.708, 0.692, and 0.705, respectively, along with area under the curve values of 80.2%, 72.7%, and 76.9% across three datasets. This model demonstrated the strongest prognostic prediction capabilities among those evaluated. Additionally, the PEX10 gene was identified as a key prognostic marker through both visual attention matrix analysis and bioinformatics methods. Our study demonstrates that the Gene Swin model effectively transforms Ribonucleic Acid (RNA) sequencing data into SIEs, enabling prognosis prediction through attention-based algorithms. This approach supports the development of a data-driven, unified, and automated model, offering a robust tool for classification and prediction tasks using RNA sequencing data. This advancement presents a novel clinical strategy for cancer treatment and prognosis forecasting.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293339","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

Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE. 使用PRONE对串联质量标签和无标签蛋白质定量数据的归一化方法进行系统评价。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf201

Lis Arend, Klaudia Adamowicz, Johannes R Schmidt, Yuliya Burankova, Olga Zolotareva, Olga Tsoy, Josch K Pauling, Stefan Kalkhof, Jan Baumbach, Markus List, Tanja Laske

{"title":"Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE.","authors":"Lis Arend, Klaudia Adamowicz, Johannes R Schmidt, Yuliya Burankova, Olga Zolotareva, Olga Tsoy, Josch K Pauling, Stefan Kalkhof, Jan Baumbach, Markus List, Tanja Laske","doi":"10.1093/bib/bbaf201","DOIUrl":"10.1093/bib/bbaf201","url":null,"abstract":"Despite the significant progress in accuracy and reliability in mass spectrometry technology, as well as the development of strategies based on isotopic labeling or internal standards in recent decades, systematic biases originating from non-biological factors remain a significant challenge in data analysis. In addition, the wide range of available normalization methods renders the choice of a suitable normalization method challenging. We systematically evaluated 17 normalization and 2 batch effect correction methods, originally developed for preprocessing DNA microarray data but widely applied in proteomics, on 6 publicly available spike-in and 3 label-free and tandem mass tag datasets. Opposed to state-of-the-art normalization practice, we found that a reduction in intragroup variation is not directly related to the effectiveness of the normalization methods. Furthermore, our results demonstrated that the methods RobNorm and Normics, specifically developed for proteomics data, in line with LoessF performed consistently well across the spike-in datasets, while EigenMS exhibited a high false-positive rate. Finally, based on experimental data, we show that normalization substantially impacts downstream analyses, and the impact is highly dataset-specific, emphasizing the importance of use-case-specific evaluations for novel proteomics datasets. For this, we developed the PROteomics Normalization Evaluator (PRONE), a unifying R package enabling comparative evaluation of normalization methods, including their impact on downstream analyses, while offering considerable flexibility, acknowledging the lack of universally accepted standards. PRONE is available on Bioconductor with a web application accessible at https://exbio.wzw.tum.de/prone/.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12058466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954339","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