Journal of Computational Biology最新文献

mcRigor: A Statistical Software Package for Evaluating and Optimizing Metacell Partitioning in Single-Cell Data Analysis. 在单细胞数据分析中评估和优化元细胞划分的统计软件包。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-09 DOI: 10.1177/15578666251383561

Pan Liu, Jingyi Jessica Li

引用次数: 0

ClusterDE: A Statistical Software Package for Removing Double-Dipping Bias in Post-Clustering Differential Expression Analysis. 聚类后差异表达分析中去除双浸偏差的统计软件包。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-08 DOI: 10.1177/15578666251383562

Christy Lee, Dongyuan Song, Siqi Chen, Jingyi Jessica Li

{"title":"ClusterDE: A Statistical Software Package for Removing Double-Dipping Bias in Post-Clustering Differential Expression Analysis.","authors":"Christy Lee, Dongyuan Song, Siqi Chen, Jingyi Jessica Li","doi":"10.1177/15578666251383562","DOIUrl":"https://doi.org/10.1177/15578666251383562","url":null,"abstract":"Typical pipelines for single-cell and spatial transcriptomics involve clustering cells or spatial spots, followed by post-clustering differential expression (DE) analysis to identify marker genes for annotating clusters as cell types or spatial domains. However, using the same data for both clustering and DE analysis-a problem known as double-dipping-can lead to spurious detection of DE genes. In particular, over-clustering can produce artificial clusters that are incorrectly interpreted as distinct cell types or spatial domains. To address this issue, the ClusterDE R package implements a statistical method using a synthetic null dataset, which consists of a single homogeneous cell population or spatial domain but is constructed to match the real dataset in terms of gene means, variances, and gene-gene rank correlations. By serving as a parallel negative control, the synthetic null data allow users to identify and remove false-positive DE genes arising from double-dipping. This article introduces the ClusterDE R package and provides practical guidance on installation and usage for more reliable marker gene detection following clustering.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145251583","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

Bayesian Validation of Dynamic Systems for Biological Networks. 生物网络动态系统的贝叶斯验证。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-03 DOI: 10.1177/15578666251382251

Donghui Son, Jaejik Kim

{"title":"Bayesian Validation of Dynamic Systems for Biological Networks.","authors":"Donghui Son, Jaejik Kim","doi":"10.1177/15578666251382251","DOIUrl":"https://doi.org/10.1177/15578666251382251","url":null,"abstract":"Dynamic systems encompass a broad class of mathematical models used to describe the behavior of complex networks or systems over time. One of the most common approaches to modeling such dynamics is through a set of ordinary differential equations (ODEs), typically constructed based on hypotheses, known interactions, or observed trajectories. However, ODEs are deterministic and inflexible, while biological data are typically noisy. Thus, the model fit might not account for all possible data variations, and there might be a discrepancy between the actual biological process and the assumed model. This discrepancy could lead to inaccuracies in the prediction and interpretation of the biological networks. Therefore, it is required to validate ODE models in terms of observed data. Given that biological networks typically involve multiple sources of errors and uncertainties, the validation process should account for these factors. The Bayesian approaches offer a robust framework for quantifying errors and uncertainties. Thus, in this study, we propose a Bayesian validation method for ODE models that addresses model inadequacy, presented as bias. Since the proposed method estimates bias as a function of time, it can provide prediction bounds for the entire observed time interval. Consequently, it allows for a direct evaluation of the model's validity across the whole time interval, and it can lead to better prediction by correcting the bias.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225417","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

BCtypeFinder: A Semi-Supervised Model with Domain Adaptation for Breast Cancer Subtyping Using DNA Methylation Profiles. BCtypeFinder：利用DNA甲基化谱进行乳腺癌亚型分型的半监督域适应模型。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-03 DOI: 10.1177/15578666251380233

Joung Min Choi, Liqing Zhang

{"title":"BCtypeFinder: A Semi-Supervised Model with Domain Adaptation for Breast Cancer Subtyping Using DNA Methylation Profiles.","authors":"Joung Min Choi, Liqing Zhang","doi":"10.1177/15578666251380233","DOIUrl":"https://doi.org/10.1177/15578666251380233","url":null,"abstract":"Accurate breast cancer subtype prediction is critical for precise diagnosis, treatment planning, and prognosis evaluation. Recent studies highlight the important role of epigenetic modifications in breast tumor, especially the potential of abnormal DNA methylation patterns as markers for distinct subtypes. However, developing a reliable model for subtype prediction based on DNA methylation profiles is challenging due to the scarcity of annotated dataset. This work proposes BCtypeFinder, a breast cancer subtype prediction framework that utilizes a domain adaptation network combined with semi-supervised learning to address batch effects. Our model leverages both labeled and unlabeled DNA methylation data to extract domain-invariant features while aligning subtype distributions across various datasets. BCtypeFinder outperforms current methods, showcasing superior classification performance across multiple test cases. Furthermore, we explored the effects of batch correction in BCtypeFinder, demonstrating its ability to remove batch-specific variations among patients of the same subtype, thus improving the robustness of the classifier. BCtypeFinder is publicly available at https://github.com/joungmin-choi/BCtypeFinder.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145225388","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

Corrigendum to: CerviNet: A Novel Approach for Cervical Cancer Classification Using Pap-Smear Images. 宫颈：一种使用宫颈涂片图像进行宫颈癌分类的新方法的勘误。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-01 DOI: 10.1177/15578666251387096

引用次数: 0

Enhanced Interpretable Neural Network Approach for Unified Batch Effect Mitigation and Disease Classification Using Cross-Cohort Microbiome Profiles. 基于跨队列微生物组谱的统一批次效应缓解和疾病分类的增强可解释神经网络方法。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-01 Epub Date: 2025-08-08 DOI: 10.1177/15578666251364292

Daryl L X Fung, Mohd Wasif Khan, Carson Kai-Sang Leung, Pingzhao Hu

{"title":"Enhanced Interpretable Neural Network Approach for Unified Batch Effect Mitigation and Disease Classification Using Cross-Cohort Microbiome Profiles.","authors":"Daryl L X Fung, Mohd Wasif Khan, Carson Kai-Sang Leung, Pingzhao Hu","doi":"10.1177/15578666251364292","DOIUrl":"10.1177/15578666251364292","url":null,"abstract":"The oral microbiome is a complex environment that consists of diverse microorganisms inhabiting the oral cavity. There are more than 700 different species of bacteria living in the oral cavity which provides nutrition to the microorganisms living in the mouth. As samples tend to be collected with a variation in non-biological factors, batch effects will occur. Batch effects are variations in the same samples, where the variations are affected by the differences in equipment used, the time when the samples were collected, the laboratory conditions, etc. Batch effects can be difficult to address as the variation might not be apparent in individual samples but rather as a whole group between samples. Several research has been proposed to resolve the batch effect, but they tend to require a two-step approach (batch effect removal, and classification), or will suffer from dropout events in gene expressions. In this study, we propose a one-step approach that combines both the batch effect removal and disease classification, eliminating the need for a two-step approach process. LassoNet was used with batch loss to mitigate the effect of batch effect and to classify disease outcome on oral microbiome simultaneously. The model achieved better performance than our baseline models, reaching 0.8 area under the curve on average on the five studies of oral microbiome. In addition, another key aspect of using LassoNet is its ability to carry out feature importance analysis, which is capable to reveal key oral microbiomes associated with disease outcomes.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"951-964"},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144804234","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

Network-Guided Sparse Subspace Clustering on Single-Cell Data. 单cell数据的网络引导稀疏子空间聚类。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-01 Epub Date: 2025-07-15 DOI: 10.1177/15578666251359688

Chenyang Yuan, Shunzhou Jiang, Songyun Li, Jicong Fan, Tianwei Yu

引用次数: 0

A Biologically Informed and Efficient DNA Sequence Learner for Predicting Functional Genomics Events. 一个生物学知情和有效的DNA序列学习者预测功能基因组学事件。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-01 Epub Date: 2025-09-24 DOI: 10.1177/15578666251382249

Mohammad Shiri, Jiangwen Sun

{"title":"A Biologically Informed and Efficient DNA Sequence Learner for Predicting Functional Genomics Events.","authors":"Mohammad Shiri, Jiangwen Sun","doi":"10.1177/15578666251382249","DOIUrl":"10.1177/15578666251382249","url":null,"abstract":"Elucidating the functional mechanisms underlying most associations between phenomes and genomes uncovered by genome-wide association studies remains a challenging problem. Deep neural networks that excel in feature learning from sequential data have recently emerged as promising approaches to addressing this challenge by mapping sequence patterns in DNA to functional genomic events. Despite the impressive progress made in this regard, the existing studies are largely limited to examining a type of network architecture that primarily consists of simple stacked convolutional layers of filters of a uniform size. These networks lack the consideration of specifics in the mapping of DNA sequences to functional genomic events, thereby impairing the learning efficiency of these networks. To address this problem, in this article, we propose an efficient DNA sequence learner (EDSL), a novel biologically informed architecture that (1) introduces filters of varying sizes in the first convolutional layer to enhance the learning of sequence patterns of diverse sizes and (2) utilizes dense connections to facilitate the participation of sequence patterns at varying levels in prediction. Our results regarding both synthetic data and a dataset consisting of 367 experimentally derived functional genomic profiles demonstrate the effectiveness of the proposed design choices and the superiority of the EDSL over existing networks in terms of both prediction performance and sequence pattern learning. Moreover, our ablation study indicates that both the proposed design choices enhance learning-importantly, in a differential and complementary manner.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"965-973"},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145137771","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

MFF-HPO: Protein-Phenotype Associations Prediction Based on Sequence Using Multi-Feature Fusion. MFF-HPO：基于多特征融合序列的蛋白质表型关联预测。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-01 Epub Date: 2025-06-30 DOI: 10.1089/cmb.2024.0883

Xuehua Bi, Zhuocheng Ji, Linlin Zhang, Guanglei Yu, Zhipeng Gao, Kai Zhao

{"title":"MFF-HPO: Protein-Phenotype Associations Prediction Based on Sequence Using Multi-Feature Fusion.","authors":"Xuehua Bi, Zhuocheng Ji, Linlin Zhang, Guanglei Yu, Zhipeng Gao, Kai Zhao","doi":"10.1089/cmb.2024.0883","DOIUrl":"10.1089/cmb.2024.0883","url":null,"abstract":"Protein abnormalities disrupt various cellular and contribute to disease development. Identifying disease-associated proteins is crucial for precision medicine, but traditional methods are time-consuming and costly, necessitating computational approaches. Existing computational methods rely on manual feature engineering and fail to leverage deep features from amino acid sequences and protein structures. In this article, we propose Model for predicting protein-phenotype associations by Fusing multi-view Features (MFF-HPO), a model for predicting protein-phenotype associations by fusing multi-view features from amino acid sequences. First, we generate three-dimensional protein structure from amino acid sequence to derive contact graphs and secondary structures then integrate these with direct sequence encoding and physicochemical properties. Using a Graph Attention Network, we extract structural features from contact graphs, while deep neural networks capture global and local features from secondary structures, physicochemical properties, and sequence encoding. Finally, concatenated features are used to predict phenotype annotations. MFF-HPO outperforms state-of-the-art methods with a mean area under the precision-recall curve of 0.314 and a mean Fmax of 0.371. Ablation studies confirm that multi-view feature fusion enhances predictions, and case studies validate its practicality.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"913-922"},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528223","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

Leveraging an Image-Enhanced Cross-Modal Fusion Network for Radiology Report Generation. 利用图像增强的跨模态融合网络生成放射学报告。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2025-10-01 Epub Date: 2025-08-11 DOI: 10.1177/15578666251365959

Yi Guo, Xiaodi Hou, Zhi Liu, Yijia Zhang

{"title":"Leveraging an Image-Enhanced Cross-Modal Fusion Network for Radiology Report Generation.","authors":"Yi Guo, Xiaodi Hou, Zhi Liu, Yijia Zhang","doi":"10.1177/15578666251365959","DOIUrl":"10.1177/15578666251365959","url":null,"abstract":"Radiology report generation (RRG) tasks leverage computer-aided technology to automatically produce descriptive text reports for medical images, aiming to ease radiologists' workload, reduce misdiagnosis rates, and lessen the pressure on medical resources. However, previous works have yet to focus on enhancing feature extraction of low-quality images, incorporating cross-modal interaction information, and mitigating latency in report generation. We propose an Image-Enhanced Cross-Modal Fusion Network (IFNet) for automatic RRG to tackle these challenges. IFNet includes three key components. First, the image enhancement module enhances the detailed representation of typical and atypical structures in X-ray images, thereby boosting detection success rates. Second, the cross-modal fusion networks efficiently and comprehensively capture the interactions of cross-modal features. Finally, a more efficient transformer report generation module is designed to optimize report generation efficiency while being suitable for low-resource devices. Experimental results on public datasets IU X-ray and MIMIC-CXR demonstrate that IFNet significantly outperforms the current state-of-the-art methods.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"923-934"},"PeriodicalIF":1.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144816832","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