BMC Bioinformatics最新文献_第4页

Bayesian inference of mixed Gaussian phylogenetic models. 混合高斯系统发育模型的贝叶斯推断。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-04-01 DOI: 10.1186/s12859-026-06399-y

Bayu Brahmantio, Krzysztof Bartoszek, Etka Yapar

引用次数: 0

MOLT: multi-object and lineage tracking in 2D and 3D biomedical time-series imaging. 多目标和谱系跟踪在二维和三维生物医学时间序列成像。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-04-01 DOI: 10.1186/s12859-026-06434-y

Ben Bausch, Mina Naseh, Goncalo Gaspar Alves, Andreas Husch, Thomas Gillet, Michael T Heneka, Jorge Goncalves, Sergio Castro Gómez, Shekoufeh Gorgi Zadeh

引用次数: 0

DHGCMDA: a dual-view heterogeneous graph contrastive learning framework for miRNA-disease association type prediction. DHGCMDA：用于mirna -疾病关联类型预测的双视图异构图对比学习框架。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-04-01 DOI: 10.1186/s12859-026-06436-w

Yan Sun, Fanyu Zhang, Shijia Yan, Xiaotong Kong, Hanxiang Wang, Junliang Shang, Jin-Xing Liu

引用次数: 0

Machine learning for multi-omics data integration in crop improvement: a systematic review. 作物改良中多组学数据集成的机器学习：系统综述。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-04-01 DOI: 10.1186/s12859-026-06438-8

Alemu Tsega, Destaw Mullualem

引用次数: 0

A two-phase clustering procedure based on allele specific expression. 基于等位基因特异性表达的两阶段聚类方法。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-04-01 DOI: 10.1186/s12859-026-06398-z

Roberto Pagliarini, Francesco Nascimben, Alberto Policriti

{"title":"A two-phase clustering procedure based on allele specific expression.","authors":"Roberto Pagliarini, Francesco Nascimben, Alberto Policriti","doi":"10.1186/s12859-026-06398-z","DOIUrl":"10.1186/s12859-026-06398-z","url":null,"abstract":"Background: Allele Specific Expression analysis is an important tool for integrating genome and transcriptome data. It quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites, and is a powerful tool to estimate cis-regulatory diversity of alleles. Clustering algorithms can be used to identify patterns or groups of genes/samples based on their expression profiles. Depending on the structure of the data, different existing clustering algorithm can be adapted to allele specific expression data. However, no ad-hoc procedure has been developed.Results: In this work, we begin defining an expression matrix capturing allele expressions from an RNA-sequencing experiment. On this matrix, we develop a novel two-phase unsupervised clustering procedure, built on top of a spectral clustering algorithm, whose aim is to partition the population into groups of similar individuals, according to their allelic expression. As case-studies, the approach is used to cluster 98 cultivars representative of the variability observed in Vitis vinifera, starting from read counts of genes of chromosome 1 of leaves, and to analyze allele-specific count data from a CASTxMRL F1 hybrid mice dataset.Conclusion: Using the above mentioned real case-studies as well as generated synthetic data, we see that our algorithm shows significant robustness and outperforms other standard clustering techniques.","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"27 Suppl 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13040796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147590012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

A comparative analysis of topological domain callers over RNA-associated interactome. rna相关相互作用组拓扑域调用者的比较分析。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-03-28 DOI: 10.1186/s12859-026-06432-0

Tuna Alaygut, Emre Sefer

{"title":"A comparative analysis of topological domain callers over RNA-associated interactome.","authors":"Tuna Alaygut, Emre Sefer","doi":"10.1186/s12859-026-06432-0","DOIUrl":"https://doi.org/10.1186/s12859-026-06432-0","url":null,"abstract":"Background: Topological domains (TADs) are consecutive genomic locations with denser local interactions to a certain extent, and they are important for cellular gene expression control and modulation. TADs were first identified when studying three-dimensional genomic structures over Hi-C interaction datasets. Many studies have focused on developing approaches in inferring TADs, which led to multiple TAD-caller approaches development. On the other hand, the number of RNA interactome datasets, such as RNA-RNA and RNA-DNA interactions, has recently been increasing. Even though TADs have been extensively studied in Hi-C datasets, they have not been studied across these RNA interactomes.Result: We conducted a systematized comparison of 28 TAD-callers across mammalians over RNA-associated interactomes (RAIs), especially RNA-RNA and RNA-DNA interaction datasets at a high resolution. Our findings highlighted the significant enrichment of Cohesin/CTCF proteins at RNA-TAD boundaries for both RNA-RNA and RNA-DNA interactomes, especially those with corner dots, which are similar to the results for original TADs. The sizes and numbers of RNA-TADs vary significantly between different TAD caller approaches and RNA interactome resolution, suggesting the importance of considering RNA-TADs as hierarchical domains rather than distinct intervals.Conclusion: We examined the core principles and assumptions behind TAD-callers over RNA interactomes. To our best knowledge, this is the first time that many TAD inference methods are adapted to infer TAD-like domains on RNAs. Our results provide valuable guidance in selecting the most suitable methods for TAD inference over RNA interactomes.","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

AutoPrompt-SAM3D: integrated generation and selection for SAM2-based 3D medical segmentation. AutoPrompt-SAM3D：基于sam2的三维医学分割集成生成和选择。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-03-28 DOI: 10.1186/s12859-026-06390-7

Wanqiu Cheng, Jintao Tang, Ting Wang, Shasha Li, Ting Deng

{"title":"AutoPrompt-SAM3D: integrated generation and selection for SAM2-based 3D medical segmentation.","authors":"Wanqiu Cheng, Jintao Tang, Ting Wang, Shasha Li, Ting Deng","doi":"10.1186/s12859-026-06390-7","DOIUrl":"https://doi.org/10.1186/s12859-026-06390-7","url":null,"abstract":"Background: The potential of Segment Anything Model 2 (SAM2) for 3D medical image segmentation via video-stream processing is currently constrained by its reliance on manual prompts. While existing research employs auxiliary models (e.g., YOLO) as prompt generators, these approaches face two fundamental limitations: the inherent bottleneck of external models' feature extraction and the lack of mechanisms to prevent the propagation of erroneous prompts. Furthermore, current methods often struggle with interference from non-salient regions in complex 3D tumor datasets. This study aims to develop an automated, reliable prompt generation and sequence processing framework specifically for 3D medical imaging.Results: We propose AutoPrompt-SAM3D, featuring an Automatic Prompt Generator that hierarchically integrates SAM2's tri-layer features and a supervised confidence frames filter for reliable prompt selection. Additionally, we implement a full-sequence processing framework that progressively localizes salient regions across consecutive slices. Comprehensive experiments conducted on four public abdominal tumor datasets demonstrate that AutoPrompt-SAM3D achieves superior 3D medical segmentation performance, consistently outperforming or matching state-of-the-art prompt-based methods.Conclusions: AutoPrompt-SAM3D eliminates the dependency on manual prompts in SAM2-based 3D segmentation through hierarchical feature integration and error filtering. By enhancing both the reliability and efficiency of tumor localization, this framework provides a practical tool for large-scale medical image analysis and supports more consistent clinical decision-making.","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147572318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

MAGMDA: a multi-order adaptive graph-based miRNA-disease association prediction model. MAGMDA：基于多阶自适应图的mirna -疾病关联预测模型。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-03-28 DOI: 10.1186/s12859-026-06433-z

Xiujuan Guo, Ning Zhao, Guohua Wang, Chunlong Zhang

引用次数: 0

Triad-LMF: a hierarchical low-rank multimodal fusion framework for robust cancer subtype classification using multi-omics data. Triad-LMF：一个分层低秩多模态融合框架，用于使用多组学数据进行稳健的癌症亚型分类。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-03-26 DOI: 10.1186/s12859-026-06378-3

Xingyue Tan, Xiran Chen, Renjie Tian, Qinyu Cai, Miaoyuan Jiang, Dongqiu Yang, Lei Zhang

引用次数: 0

RCDRank: a web server to prioritize regulated cell death modalities. rcdrink：一个优先考虑受调节细胞死亡方式的web服务器。

IF 3.3 3区生物学

BMC Bioinformatics Pub Date : 2026-03-26 DOI: 10.1186/s12859-026-06428-w

Nan Zhou, Tong Yin, Huiran Sun, Qiqi Luo, Yuhong Zhang, Xiaolei Shi, Jinku Bao, Li Peng, Xiaoqing Yuan

{"title":"RCDRank: a web server to prioritize regulated cell death modalities.","authors":"Nan Zhou, Tong Yin, Huiran Sun, Qiqi Luo, Yuhong Zhang, Xiaolei Shi, Jinku Bao, Li Peng, Xiaoqing Yuan","doi":"10.1186/s12859-026-06428-w","DOIUrl":"10.1186/s12859-026-06428-w","url":null,"abstract":"Background: Regulated cell death (RCD) maintains cellular homeostasis and tissue integrity, playing a pivotal role in both health and disease. A variety of RCD subroutines have been identified, each characterized by distinct molecular and morphological features. These cell death modalities do not operate in isolation. Instead, they interact with one another in complex ways. This interaction leads to crosstalk through interconnected and often overlapping signaling pathways. Multiple forms of RCD can coexist within the same disease, influencing various cell types or even the same cell type either synchronously or sequentially. Understanding the intricate dynamics of RCD is of high importance. However, it is challenging to discern the relative contribution of various RCD modalities within the specific biological context. This necessitates the development of advanced methodologies to systematically analyze the priority of RCD pathways in various cellular environments. MAIN: In the present study, we first created a manually curated collection of gene sets for 18 well-characterized RCD modes. We then estimated the significance of each RCD pathway by combining the results of seven gene set enrichment tests via the Tippet p-value combination approach. Afterward, the consensus of enrichment for each RCD pathway across tests was evaluated using robust rank aggregation. The priorities of RCD were subsequently resolved by considering both the combined p value and the consensus score. The reliability of the proposed approach was validated by applying it to explore the RCD modes in intracranial aneurysms. Finally, a user-friendly web application was created for researchers worldwide.Conclusion: Our study offers a new way to reveal complicated RCD modalities in specific biological settings. The web server is freely accessible at https://www.zhounan.org/rcdrank.","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":" ","pages":""},"PeriodicalIF":3.3,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13141446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147519670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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