Journal of Computational Biology最新文献_第3页

Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images. 利用 SE 连接和 ASPP 的注意力引导残差 U-Net 用于显微镜图像中基于分水岭的细胞分割。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-01 Epub Date: 2024-10-18 DOI: 10.1089/cmb.2023.0446

Jovial Niyogisubizo, Keliang Zhao, Jintao Meng, Yi Pan, Rosiyadi Didi, Yanjie Wei

{"title":"Attention-Guided Residual U-Net with SE Connection and ASPP for Watershed-Based Cell Segmentation in Microscopy Images.","authors":"Jovial Niyogisubizo, Keliang Zhao, Jintao Meng, Yi Pan, Rosiyadi Didi, Yanjie Wei","doi":"10.1089/cmb.2023.0446","DOIUrl":"10.1089/cmb.2023.0446","url":null,"abstract":"Time-lapse microscopy imaging is a crucial technique in biomedical studies for observing cellular behavior over time, providing essential data on cell numbers, sizes, shapes, and interactions. Manual analysis of hundreds or thousands of cells is impractical, necessitating the development of automated cell segmentation approaches. Traditional image processing methods have made significant progress in this area, but the advent of deep learning methods, particularly those using U-Net-based networks, has further enhanced performance in medical and microscopy image segmentation. However, challenges remain, particularly in accurately segmenting touching cells in images with low signal-to-noise ratios. Existing methods often struggle with effectively integrating features across different levels of abstraction. This can lead to model confusion, particularly when important contextual information is lost or the features are not adequately distinguished. The challenge lies in appropriately combining these features to preserve critical details while ensuring robust and accurate segmentation. To address these issues, we propose a novel framework called RA-SE-ASPP-Net, which incorporates Residual Blocks, Attention Mechanism, Squeeze-and-Excitation connection, and Atrous Spatial Pyramid Pooling to achieve precise and robust cell segmentation. We evaluate our proposed architecture using an induced pluripotent stem cell reprogramming dataset, a challenging dataset that has received limited attention in this field. Additionally, we compare our model with different ablation experiments to demonstrate its robustness. The proposed architecture outperforms the baseline models in all evaluated metrics, providing the most accurate semantic segmentation results. Finally, we applied the watershed method to the semantic segmentation results to obtain precise segmentations with specific information for each cell.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"225-237"},"PeriodicalIF":1.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

A Joint Bayesian Model for Change-Points and Heteroskedasticity Applied to the Canadian Longitudinal Study on Aging. 变化点和异方差联合贝叶斯模型在加拿大老龄化纵向研究中的应用。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-20 DOI: 10.1089/cmb.2024.0563

Joosung Min, Olga Vishnyakova, Angela Brooks-Wilson, Lloyd T Elliott

{"title":"A Joint Bayesian Model for Change-Points and Heteroskedasticity Applied to the Canadian Longitudinal Study on Aging.","authors":"Joosung Min, Olga Vishnyakova, Angela Brooks-Wilson, Lloyd T Elliott","doi":"10.1089/cmb.2024.0563","DOIUrl":"https://doi.org/10.1089/cmb.2024.0563","url":null,"abstract":"Maintaining homeostasis, the regulation of internal physiological parameters, is essential for health and well-being. Deviations from optimal levels, or 'sweet spots,' can lead to health deterioration and disease. Identifying biomarkers with sweet spots requires both change-point detection and variance effect analysis. Traditional approaches involve separate tests for change-points and heteroskedasticity, which can yield inaccurate results if model assumptions are violated. To address these challenges, we propose a unified approach: Bayesian Testing for Heteroskedasticity and Sweet Spots (BTHS). This framework integrates sampling-based parameter estimation and Bayes factor computation to enhance change-point detection, heteroskedasticity quantification, and testing in change-point regression settings, and extends previous Bayesian approaches. BTHS eliminates the need for separate analyses and provides detailed insights into both the magnitude and shape of heteroskedasticity, enabling robust identification of sweet spots without strong assumptions. We applied BTHS to blood elements from the Canadian Longitudinal Study on Aging identifying nine blood elements with significant sweet spot variance effects.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

CLHGNNMDA: Hypergraph Neural Network Model Enhanced by Contrastive Learning for miRNA-Disease Association Prediction. CLHGNNMDA：通过对比学习增强的超图神经网络模型，用于 miRNA 与疾病的关联预测。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 Epub Date: 2024-11-27 DOI: 10.1089/cmb.2024.0720

Rong Zhu, Yong Wang, Ling-Yun Dai

{"title":"CLHGNNMDA: Hypergraph Neural Network Model Enhanced by Contrastive Learning for miRNA-Disease Association Prediction.","authors":"Rong Zhu, Yong Wang, Ling-Yun Dai","doi":"10.1089/cmb.2024.0720","DOIUrl":"10.1089/cmb.2024.0720","url":null,"abstract":"Numerous biological experiments have demonstrated that microRNA (miRNA) is involved in gene regulation within cells, and mutations and abnormal expression of miRNA can cause a myriad of intricate diseases. Forecasting the association between miRNA and diseases can enhance disease prevention and treatment and accelerate drug research, which holds considerable importance for the development of clinical medicine and drug research. This investigation introduces a contrastive learning-augmented hypergraph neural network model, termed CLHGNNMDA, aimed at predicting associations between miRNAs and diseases. Initially, CLHGNNMDA constructs multiple hypergraphs by leveraging diverse similarity metrics related to miRNAs and diseases. Subsequently, hypergraph convolution is applied to each hypergraph to extract feature representations for nodes and hyperedges. Following this, autoencoders are employed to reconstruct information regarding the feature representations of nodes and hyperedges and to integrate various features of miRNAs and diseases extracted from each hypergraph. Finally, a joint contrastive loss function is utilized to refine the model and optimize its parameters. The CLHGNNMDA framework employs multi-hypergraph contrastive learning for the construction of a contrastive loss function. This approach takes into account inter-view interactions and upholds the principle of consistency, thereby augmenting the model's representational efficacy. The results obtained from fivefold cross-validation substantiate that the CLHGNNMDA algorithm achieves a mean area under the receiver operating characteristic curve of 0.9635 and a mean area under the precision-recall curve of 0.9656. These metrics are notably superior to those attained by contemporary state-of-the-art methodologies.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"47-63"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142729054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Is Tumor Growth Influenced by the Bone Remodeling Process? 肿瘤生长是否受骨重塑过程的影响？

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 Epub Date: 2024-12-26 DOI: 10.1089/cmb.2023.0390

Juan Felipe Sánchez, Salah Ramtani, Abdelkader Boucetta, Marco Antonio Velasco, Juan Jairo Vaca-González, Carlos A Duque-Daza, Diego A Garzón-Alvarado

引用次数: 0

Advances in Estimating Level-1 Phylogenetic Networks from Unrooted SNPs. 从无根 SNPs 估算一级系统发育网络的进展。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 Epub Date: 2024-11-25 DOI: 10.1089/cmb.2024.0710

Tandy Warnow, Yasamin Tabatabaee, Steven N Evans

{"title":"Advances in Estimating Level-1 Phylogenetic Networks from Unrooted SNPs.","authors":"Tandy Warnow, Yasamin Tabatabaee, Steven N Evans","doi":"10.1089/cmb.2024.0710","DOIUrl":"10.1089/cmb.2024.0710","url":null,"abstract":"We address the problem of how to estimate a phylogenetic network when given single-nucleotide polymorphisms (i.e., SNPs, or bi-allelic markers that have evolved under the infinite sites assumption). We focus on level-1 phylogenetic networks (i.e., networks where the cycles are node-disjoint), since more complex networks are unidentifiable. We provide a polynomial time quartet-based method that we prove correct for reconstructing the semi-directed level-1 phylogenetic network N, if we are given a set of SNPs that covers all the bipartitions of N, even if the ancestral state is not known, provided that the cycles are of length at least 5; we also prove that an algorithm developed by Dan Gusfield in the Journal of Computer and System Sciences in 2005 correctly recovers semi-directed level-1 phylogenetic networks in polynomial time in this case. We present a stochastic model for DNA evolution, and we prove that the two methods (our quartet-based method and Gusfield's method) are statistically consistent estimators of the semi-directed level-1 phylogenetic network. For the case of multi-state homoplasy-free characters, we prove that our quartet-based method correctly constructs semi-directed level-1 networks under the required conditions (all cycles of length at least five), while Gusfield's algorithm cannot be used in that case. These results assume that we have access to an oracle for indicating which sites in the DNA alignment are homoplasy-free, and we show that the methods are robust, under some conditions, to oracle errors.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"3-27"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Endhered Patterns in Matchings and RNA. 匹配与RNA的内在模式。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 Epub Date: 2024-12-23 DOI: 10.1089/cmb.2024.0658

Célia Biane, Greg Hampikian, Sergey Kirgizov, Khaydar Nurligareev

引用次数: 0

Optimizing Metabolite Production with Neighborhood-Based Binary Quantum-Behaved Particle Swarm Optimization and Flux Balance Analysis. 基于邻域二元量子态粒子群优化和通量平衡分析的代谢物生产优化。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI: 10.1089/cmb.2024.0538

Lidan Bai, Jun Sun, Vasile Palade, Chao Li, Hengyang Lu, Cong Gao

{"title":"Optimizing Metabolite Production with Neighborhood-Based Binary Quantum-Behaved Particle Swarm Optimization and Flux Balance Analysis.","authors":"Lidan Bai, Jun Sun, Vasile Palade, Chao Li, Hengyang Lu, Cong Gao","doi":"10.1089/cmb.2024.0538","DOIUrl":"10.1089/cmb.2024.0538","url":null,"abstract":"Metabolic engineering is a rapidly evolving field that involves optimizing microbial cell factories to overproduce various industrial products. To achieve this, several tools, leveraging constraint-based stoichiometric models and metaheuristic algorithms like particle swarm optimization (PSO), have been developed. However, PSO can potentially get trapped in local optima. Quantum-behaved PSO (QPSO) overcomes this limitation, and our study further enhances its binary version (BQPSO) with a neighborhood topology, leading to the advanced neighborhood-based BQPSO (NBQPSO). Combined with flux balance analysis (FBA), this forms an innovative approach, NBQPSO-FBA, for identifying optimal knockout strategies to maximize the desired metabolite production. Additionally, we introduced a novel encoding strategy suitable for large-scale genome-scale metabolic models (GSMMs). Evaluated on four E. coli GSMMs (iJR904, iAF1260, iJO1366, and iML1515), NBQPSO-FBA matches or surpasses established bi-level linear programming (LP) and heuristic methods in metabolite production optimization. Notably, it achieved 90.69% realization of the theoretical maximum in acetate production and demonstrated comparable performance with leading algorithms in lactate production. The efficiency of NBQPSO-FBA, which requires fewer knockouts, makes it a practical and effective tool for optimizing microbial cell factories. This addresses the rising demand for microbial products across various industries.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"64-88"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142800309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Acknowledgment of Reviewers 2024. 审稿人致谢

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 DOI: 10.1089/cmb.2024.10852.revack

引用次数: 0

An Analytical Approach that Combines Knowledge from Germline and Somatic Mutations Enhances Tumor Genomic Reanalyses in Precision Oncology. 一种结合生殖系和体细胞突变知识的分析方法增强了精确肿瘤学中的肿瘤基因组再分析。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-01-01 Epub Date: 2024-12-11 DOI: 10.1089/cmb.2023.0461

Elias DeVoe, Honey V Reddi, Bradley W Taylor, Samantha Stachowiak, Jennifer L Geurts, Ben George, Reza Shaker, Raul Urrutia, Michael T Zimmermann

{"title":"An Analytical Approach that Combines Knowledge from Germline and Somatic Mutations Enhances Tumor Genomic Reanalyses in Precision Oncology.","authors":"Elias DeVoe, Honey V Reddi, Bradley W Taylor, Samantha Stachowiak, Jennifer L Geurts, Ben George, Reza Shaker, Raul Urrutia, Michael T Zimmermann","doi":"10.1089/cmb.2023.0461","DOIUrl":"10.1089/cmb.2023.0461","url":null,"abstract":"Background: Expanded analysis of tumor genomics data enables current and future patients to gain more benefits, such as improving diagnosis, prognosis, and therapeutics. Methods: Here, we report tumor genomic data from 1146 cases accompanied by simultaneous expert analysis from patients visiting our oncological clinic. We developed an analytical approach that leverages combined germline and cancer genetics knowledge to evaluate opportunities, challenges, and yield of potentially medically relevant data. Results: We identified 499 cases (44%) with variants of interest, defined as either potentially actionable or pathogenic in a germline setting, and that were reported in the original analysis as variants of uncertain significance (VUS). Of the 7405 total unique tumor variants reported, 462 (6.2%) were reported as VUS at the time of diagnosis, yet information from germline analyses identified them as (likely) pathogenic. Notably, we find that a sizable number of these variants (36%-79%) had been reported in heritable disorders and deposited in public databases before the year of tumor testing. Conclusions: This finding indicates the need to develop data systems to bridge current gaps in variant annotation and interpretation and to develop more complete digital representations of actionable pathways. We outline our process for achieving such methodologic integration. Sharing genomics data across medical specialties can enable more robust, equitable, and thorough use of patient's genomics data. This comprehensive analytical approach and the new knowledge derived from its results highlight its multi-specialty value in precision oncology settings.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"89-103"},"PeriodicalIF":1.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

De Novo Antibody Design with SE(3) Diffusion. 基于SE(3)扩散的从头抗体设计。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-27 DOI: 10.1089/cmb.2024.0768

Daniel Cutting, Frédéric A Dreyer, David Errington, Constantin Schneider, Charlotte M Deane

引用次数: 0