Briefings in bioinformatics最新文献_第2页

Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization. 基于残差阈值深度矩阵分解的药物基因组学数据大规模信息检索与校正。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf226

Zhiyue Tom Hu, Yaodong Yu, Ruoqiao Chen, Shan-Ju Yeh, Bin Chen, Haiyan Huang

{"title":"Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization.","authors":"Zhiyue Tom Hu, Yaodong Yu, Ruoqiao Chen, Shan-Ju Yeh, Bin Chen, Haiyan Huang","doi":"10.1093/bib/bbaf226","DOIUrl":"https://doi.org/10.1093/bib/bbaf226","url":null,"abstract":"Pharmacogenomics studies are attracting an increasing amount of interest from researchers in precision medicine. The advances in high-throughput experiments and multiplexed approaches allow the large-scale quantification of drug sensitivities in molecularly characterized cancer cell lines (CCLs), resulting in a number of open drug sensitivity datasets for drug biomarker discovery. However, a significant inconsistency in drug sensitivity values among these datasets has been noted. Such inconsistency indicates the presence of substantial noise, subsequently hindering downstream analyses. To address the noise in drug sensitivity data, we introduce a robust and scalable deep learning framework, Residual Thresholded Deep Matrix Factorization (RT-DMF). This method takes a single drug sensitivity data matrix as its sole input and outputs a corrected and imputed matrix. Deep matrix factorization (DMF) excels at uncovering subtle patterns, due to its minimal reliance on data structure assumptions. This attribute significantly boosts DMF's ability to identify complex hidden patterns among nuisance effects in the data, thereby facilitating the detection of signals that are therapeutically relevant. Furthermore, RT-DMF incorporates an iterative residual thresholding procedure, which plays a crucial role in retaining signals more likely to hold therapeutic importance. Validation using simulated datasets and real pharmacogenomics datasets demonstrates the effectiveness of our approach in correcting noise and imputing missing data in drug sensitivity datasets (open-source package available at https://github.com/tomwhoooo/rtdmf).","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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149295","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

Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification. 基于智能机器学习分类的混杂聚合抑制分析。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf205

Luxuan Wang, Beihong Ji, Jingchen Zhai, Junmei Wang

{"title":"Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification.","authors":"Luxuan Wang, Beihong Ji, Jingchen Zhai, Junmei Wang","doi":"10.1093/bib/bbaf205","DOIUrl":"10.1093/bib/bbaf205","url":null,"abstract":"Small molecules have been playing a crucial role in drug discovery; however, some exhibit nonspecific inhibitory effects during hit screening due to the formation of colloidal aggregators. Such false positives often lead to significant research costs and time investment. Therefore, to identify potential aggregating compounds efficiently and accurately at an early stage of drug discovery, we employed several machine learning techniques to develop classification models for identifying promiscuous aggregating inhibitors. Using a training dataset of 10 000 aggregators and 10 000 nonaggregators, models were trained by combining four different molecular representations with various machine learning algorithms. We found that the best-performing model is the one that employs path-based FP2 fingerprints in conjunction with the cubic support vector machine algorithm, which achieved the highest accuracy and area under the receiver operating characteristic curve values for both the validation and test datasets while maintaining high sensitivity and specificity levels (>0.93). Additionally, we have proposed a new model interpretation method, global sensitivity analysis (GSA), to complement the well-recognized SHapley Additive exPlanations analysis. Several comparative studies have shown that GSA is a time-efficient and accurate approach for identifying crucial descriptors that contribute to model prediction, especially in the scenario where the dataset contains a substantial number of data entries with a limited set of descriptors. Our models as well as GSA findings can provide useful guidance on screening library design to minimize false positives.","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/PMC12056367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961831","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

Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering. 基于多视图对比学习的解耦GNNs用于scRNA-seq数据聚类。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf198

Xiaoyan Yu, Yixuan Ren, Min Xia, Zhenqiu Shu, Liehuang Zhu

{"title":"Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering.","authors":"Xiaoyan Yu, Yixuan Ren, Min Xia, Zhenqiu Shu, Liehuang Zhu","doi":"10.1093/bib/bbaf198","DOIUrl":"10.1093/bib/bbaf198","url":null,"abstract":"Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing graph neural networks (GNNs) for cell clustering, the dependencies between cells expand exponentially with the number of layers. This results in high computational complexity, negatively impacting the model's training efficiency. To address these challenges, we propose a novel approach, called decoupled GNNs, based on multi-view contrastive learning (scDeGNN), for scRNA-seq data clustering. Firstly, this method constructs two adjacency matrices to generate distinct views, and trains them using decoupled GNNs to derive the initial cell feature representations. These representations are then refined through a multilayer perceptron and a contrastive learning layer, ensuring the consistency and discriminability of the learned features. Finally, the learned representations are fused and applied to the cell clustering task. Extensive experimental results on nine real scRNA-seq datasets from various organisms and tissues show that the proposed scDeGNN method significantly outperforms other state-of-the-art scRNA-seq data clustering algorithms across multiple evaluation metrics.","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/PMC12077398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144076070","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

Knowledge-guided multi-level network modeling with experimental characterization identifies PRKCA as a novel biomarker and tumor suppressor triggering ferroptosis in prostate cancer. 知识引导的多层次网络模型与实验表征确定了PRKCA作为一种新的生物标志物和肿瘤抑制因子引发前列腺癌铁下垂。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf220

Yuxin Lin, Zongming Jia, Jixiang Wu, Hubo Yang, Xin Chen, He Wang, Xuedong Wei, Wenying Yan, Xin Qi, Yuhua Huang

{"title":"Knowledge-guided multi-level network modeling with experimental characterization identifies PRKCA as a novel biomarker and tumor suppressor triggering ferroptosis in prostate cancer.","authors":"Yuxin Lin, Zongming Jia, Jixiang Wu, Hubo Yang, Xin Chen, He Wang, Xuedong Wei, Wenying Yan, Xin Qi, Yuhua Huang","doi":"10.1093/bib/bbaf220","DOIUrl":"10.1093/bib/bbaf220","url":null,"abstract":"Prostate cancer (PCa) is observed with high incidence in men worldwide. Ferroptosis, occurred from disorders in a series of gene and pathway regulation, is an emerging target against cancer. However, most of the computational approaches solely treated ferroptosis-related genes (FRGs) as independent variables in model training, and the interactions among FRGs and other candidates were not fully deciphered in a disease-specific content. In this study, a novel network-based and knowledge-guided bioinformatics model was proposed by integrating ferroptosis-related prior knowledge with topological and functional characterization on a protein-protein interaction network for biomarker discovery in PCa development and ferroptosis. The model started at a random walk with restart algorithm for weighting genes close to known FRGs in the PCa-specific network to extract a core subnetwork for robustness and vulnerability analysis. Then key regulatory modules and a candidate gene, i.e. PRKCA, were respectively identified using a multi-level prioritization strategy with hub-bottleneck node filtering, edge-based gene co-expression measuring, community module detecting and a newly defined Ferr.neighbor functional score. The experimental validation using human clinical samples, cell lines, and nude mice convinced the role of PRKCA as a latent biomarker and a tumor suppressor in PCa carcinogenesis with a potential mechanism on triggering GPX4-mediated ferroptosis of PCa cells. This study provides a general-purpose systems biology framework for significant FRG screening, and future translational perspectives of PRKCA as a novel diagnostic and therapeutic signature for PCa management should be explored.","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/PMC12090055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109750","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

Editor's Note on 'Bioinformatics in Russia: history and present-day landscape'. 编者对“俄罗斯生物信息学：历史和当今景观”的注释。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf181

引用次数: 0

Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2. 利用HighFold2预测含非天然氨基酸环肽的结构。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf202

Cheng Zhu, Sen Cao, Tianfeng Shang, Jingjing Guo, An Su, Chengxi Li, Hongliang Duan

{"title":"Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2.","authors":"Cheng Zhu, Sen Cao, Tianfeng Shang, Jingjing Guo, An Su, Chengxi Li, Hongliang Duan","doi":"10.1093/bib/bbaf202","DOIUrl":"10.1093/bib/bbaf202","url":null,"abstract":"Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the 3D structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for these residues. Then, it incorporates an additional neural network to characterize the atom-level features of peptides, allowing for multi-scale modeling of peptide molecules while enabling the distinction between various unnatural amino acids. Besides, HighFold2 constructs a relative position encoding matrix for cyclic peptides based on different cyclization constraints. Except for training using spatial structures with unnatural amino acids, HighFold2 also parameterizes the unnatural amino acids to relax the predicted structure by energy minimization for clash elimination. Extensive empirical experiments demonstrate that HighFold2 can accurately predict the 3D structures of cyclic peptide monomers containing unnatural amino acids and their complexes with proteins, with the median RMSD for Cα reaching 1.891 Å. All these results indicate the effectiveness of HighFold2, representing a significant advancement in cyclic peptide-based drug discovery.","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/PMC12066415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977203","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

Accounting for the impact of rare variants on causal inference with RARE: a novel multivariable Mendelian randomization method. 用一种新的多变量孟德尔随机化方法来解释罕见变量对因果推理的影响。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf214

Yu Cheng, Xinjia Ruan, Xiaofan Lu, Yuqing Yang, Yuhang Wang, Shangjin Yan, Yuzhe Sun, Fangrong Yan, Liyun Jiang, Tiantian Liu

{"title":"Accounting for the impact of rare variants on causal inference with RARE: a novel multivariable Mendelian randomization method.","authors":"Yu Cheng, Xinjia Ruan, Xiaofan Lu, Yuqing Yang, Yuhang Wang, Shangjin Yan, Yuzhe Sun, Fangrong Yan, Liyun Jiang, Tiantian Liu","doi":"10.1093/bib/bbaf214","DOIUrl":"10.1093/bib/bbaf214","url":null,"abstract":"Mendelian randomization (MR) method utilizes genetic variants as instrumental variables to infer the causal effect of an exposure on an outcome. However, the impact of rare variants on traits is often neglected, and traditional MR assumptions can be violated by correlated horizontal pleiotropy (CHP) and uncorrelated horizontal pleiotropy (UHP). To address these issues, we propose a multivariable MR approach, an extension of the standard MR framework: MVMR incorporating Rare variants Accounting for multiple Risk factors and shared horizontal plEiotropy (RARE). In the simulation studies, we demonstrate that RARE effectively detects the causal effects of exposures on outcome with accounting for the impact of rare variants on causal inference. Additionally, we apply RARE to study the effects of high density lipoprotein and low density lipoprotein on type 2 diabetes and coronary atherosclerosis, respectively, thereby illustrating its robustness and effectiveness in real data analysis.","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/PMC12078940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144076068","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

Influenza virus reassortment patterns exhibit preference and continuity while uncovering cross-species transmission events. 流感病毒重组模式在揭示跨物种传播事件时表现出偏好和连续性。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf233

Xiao Ding, Yun Ma, Shicheng Li, Jingze Liu, Luyao Qin, Aiping Wu

{"title":"Influenza virus reassortment patterns exhibit preference and continuity while uncovering cross-species transmission events.","authors":"Xiao Ding, Yun Ma, Shicheng Li, Jingze Liu, Luyao Qin, Aiping Wu","doi":"10.1093/bib/bbaf233","DOIUrl":"10.1093/bib/bbaf233","url":null,"abstract":"Genomic reassortment is a key driver of influenza virus evolution and a major factor in pandemic emergence, as reassorted strains can exhibit significantly altered antigenicity. However, due to technical and ethical constraints, research on reassortment patterns (RPs) has been limited, impeding effective surveillance and control strategies. To address this gap, we developed FluRPId, a framework for identifying RPs based on the genetic diversity of influenza viruses. FluRPId integrates principles of reassortment diversity maximization, dominance, and epidemiological likelihood to assess the credibility of detected reassortment events. Applying FluRPId, we constructed a comprehensive reassortment landscape of influenza viruses, encompassing widespread reassortment events with high credibility, which also include most previously reported reassortment events. Our analysis revealed that the NS gene frequently reassorts with PA and NA, while reassortment involving HA, NA, and NS occurs more frequently than expected. Furthermore, we identified specific loci combinations that exhibit strong linkage during reassortment, providing insights into segment association preferences. Additionally, extensive reassortment chains were observed across all subtypes, underscoring the continuity of reassortment in influenza virus evolution. Notably, we identified significant cross-species reassortment events and characterized host adaptation changes in cross-species-transmitted viruses. Our study provides the most comprehensive reassortment landscape of influenza viruses to date, uncovering key patterns, preferences, and evolutionary continuity. These findings bridge a critical gap in macro-scale reassortment studies and offer insights for future research and control efforts.","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/PMC12096011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118813","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

Network analysis of multivariate time series data in biological systems: methods and applications. 生物系统中多变量时间序列数据的网络分析：方法和应用。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf223

Hao Mei, Zhiyuan Wang, Hang Yang, Xiaoke Li, Yaqing Xu

{"title":"Network analysis of multivariate time series data in biological systems: methods and applications.","authors":"Hao Mei, Zhiyuan Wang, Hang Yang, Xiaoke Li, Yaqing Xu","doi":"10.1093/bib/bbaf223","DOIUrl":"10.1093/bib/bbaf223","url":null,"abstract":"Network analysis has become an essential tool in biological and biomedical research, providing insights into complex biological mechanisms. Since biological systems are inherently time-dependent, incorporating time-varying methods is crucial for capturing temporal changes, adaptive interactions, and evolving dependencies within networks. Our study explores key time-varying methodologies for network structure estimation and network inference based on observed structures. We begin by discussing approaches for estimating network structures from data, focusing on the time-varying Gaussian graphical model, dynamic Bayesian network, and vector autoregression-based causal analysis. Next, we examine analytical techniques that leverage pre-specified or observed networks, including other autoregression-based methods and latent variable models. Furthermore, we explore practical applications and computational tools designed for these methods. By synthesizing these approaches, our study provides a comprehensive evaluation of their strengths and limitations in the context of biological data analysis.","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/PMC12096012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144118818","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

On "Bioinformatics in Russia: history and present-day landscape" by M.A. Nawaz, I.E. Pamirsky, and K.S. Golokhvast. 关于“俄罗斯的生物信息学：历史和当今景观”，作者：M.A. Nawaz， I.E. Pamirsky和K.S. Golokhvast。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2025-05-01 DOI: 10.1093/bib/bbaf161

Mikhail S Gelfand

引用次数: 0