Journal of Computational Biology最新文献_第4页

Bias in Genome-Wide Association Test Statistics Due to Omitted Interactions. 全基因组关联检验统计中由于忽略相互作用而产生的偏差。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-08-28 DOI: 10.1177/15578666261479749

Burak Yelmen, Merve Nur Güler, Tõnu Kollo, Märt Möls, Guillaume Charpiat, Flora Jay

{"title":"Bias in Genome-Wide Association Test Statistics Due to Omitted Interactions.","authors":"Burak Yelmen, Merve Nur Güler, Tõnu Kollo, Märt Möls, Guillaume Charpiat, Flora Jay","doi":"10.1177/15578666261479749","DOIUrl":"https://doi.org/10.1177/15578666261479749","url":null,"abstract":"Over the past two decades, genome-wide association studies (GWAS) enabled the discovery of thousands of variants associated with many complex human traits. However, conventional GWAS are still widely performed with linear models with the assumption that the genetic effects are predominantly additive. In this work, we investigate the test statistic behavior when linear models are used to obtain significant genotype-phenotype associations without accounting for epistasis. We first algebraically derive mean and variance shift in the null statistic due to the omitted interaction term and define the boundary between conservative (i.e., deflated statistic tail) and anti-conservative (i.e., inflated statistic tail) regimes for the common GWAS significance threshold. We then perform phenotype simulation analyses using the Estonian Biobank genotypes and validate the mathematical model. We demonstrate that the anti-conservative regime is plausible under realistic parameter settings and models omitting interaction terms can produce spurious significance. Our findings suggest caution when interpreting statistically significant signals reported in the literature based on linear models, especially for large-scale GWAS.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261479749"},"PeriodicalIF":1.3,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850816","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

Model Selection and Parameter Estimation for Fractional SIR Model Based on the Combination of Reinforcement Learning and ABC-SMC. 基于强化学习和ABC-SMC相结合的分数阶SIR模型选择与参数估计。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-08-01 Epub Date: 2026-05-08 DOI: 10.1177/15578666261444393

Peiqi Chen, Wei Gu

引用次数: 0

Exploring Intratumor Heterogeneity in Cancer: A Comparative Evaluation of Clustering Methods. 探讨肿瘤内异质性：聚类方法的比较评价。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-08-01 Epub Date: 2026-05-12 DOI: 10.1177/15578666261449274

Paulo Henrique Ribeiro, Adenilso da Silva Simao

{"title":"Exploring Intratumor Heterogeneity in Cancer: A Comparative Evaluation of Clustering Methods.","authors":"Paulo Henrique Ribeiro, Adenilso da Silva Simao","doi":"10.1177/15578666261449274","DOIUrl":"10.1177/15578666261449274","url":null,"abstract":"Intratumor heterogeneity (ITH) impacts cancer progression, and its characterization is crucial. Clustering algorithms applied to the variant allele frequency (VAF) of mutations can facilitate the exploratory analysis of ITH. This study comparatively evaluated six clustering algorithms to characterize ITH by clustering mutations based on their VAFs. We utilized data from The Cancer Genome Atlas to analyze three cancer types by examining the distribution of clusters in the results from various methods and four internal validation metrics. The results indicated that the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Variational Bayesian Gaussian Mixture Model methods identified an insufficient number of clusters in most tumor samples. The Hierarchical DBSCAN (HDBSCAN) and Ordering Points to Identify the Clustering Structure (OPTICS) algorithms exhibited greater variability in the number of clusters, while Affinity Propagation (AP) showed controlled behavior, and Mean-Shift demonstrated greater consistency. The Mean-Shift and AP methods were consistently superior in the validation metrics, in contrast to HDBSCAN and OPTICS, which had inferior performance. We conclude that Mean-Shift and AP are promising and accessible alternatives for the initial exploratory analysis of ITH by VAFs. A computational pipeline is provided on the Google Colab platform to facilitate future studies.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"787-803"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147874222","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

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data. 单细胞RNA测序数据的深度结构增强细胞聚类模型。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-08-01 Epub Date: 2026-06-09 DOI: 10.1177/15578666261453098

Maoxuan Yao, Lina Ren

引用次数: 0

TransGAT-DTI: Transformer and Graph Attention Network for Drug-Target Interaction Prediction. TransGAT-DTI：用于药物-靶标相互作用预测的变压器和图注意网络。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-08-01 Epub Date: 2026-05-08 DOI: 10.1177/15578666261445112

Changjian Zhou, Shuoxiang Wang, Yujie Zhong, Wensheng Xiang

{"title":"TransGAT-DTI: Transformer and Graph Attention Network for Drug-Target Interaction Prediction.","authors":"Changjian Zhou, Shuoxiang Wang, Yujie Zhong, Wensheng Xiang","doi":"10.1177/15578666261445112","DOIUrl":"10.1177/15578666261445112","url":null,"abstract":"Drug-target interaction (DTI) prediction is of great practical value for discovering, developing, and repurposing drugs, which has tremendous advantages to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. To date, numerous machine learning-based approaches show promising performance, which greatly improves DTI discovery efficiency, but there are still two challenges remained. One is how to represent the spatial structure features of drugs and targets appropriately, and the other is how to explicitly and effectively model and learn local interactions between drugs and targets, for better interpretation and prediction. In this work, we propose a novel framework that combines a transformer and graph attention convolutional network for DTI prediction (TransGAT-DTI), which not only precisely predicts the putative DTIs with satisfactory overall performance on three benchmark datasets, but also captures the pivotal sequence that contributes the most to the positive predictions. Experimental results demonstrate that the proposed TransGAT-DTI achieves the best performance on BindingDB, BioSNAP, and Human datasets. Importantly, the proposed approach provides a novel solution for discovering and developing novel drugs.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"834-853"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838630","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

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks. PPIGAN：利用生成对抗网络预测蛋白质-蛋白质相互作用。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-08-01 Epub Date: 2026-06-09 DOI: 10.1177/15578666261453608

Xu Zhang, Songyan Xue, Jing Geng, Xiehuizhi Wen, Lingwei Lai, Lvwen Huang, Jiantao Yu

{"title":"PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.","authors":"Xu Zhang, Songyan Xue, Jing Geng, Xiehuizhi Wen, Lingwei Lai, Lvwen Huang, Jiantao Yu","doi":"10.1177/15578666261453608","DOIUrl":"10.1177/15578666261453608","url":null,"abstract":"The prediction of protein-protein interaction (PPI) can be insightful for exploring the molecular mechanisms of cellular functions. Constructing the negative datasets of PPI is related to the assessment of the prediction accuracy and evaluation of the prediction performance. Aiming at the problem of unstable prediction accuracy in the current method of building negative sets using random sampling, we proposed a method of constructing negative sets based on a conditional generative adversarial network (CGAN), named PPIGAN. This method generates negative samples through a generative network, and the PPI prediction model uses these generated negative samples along with positive samples to learn interaction features. Simultaneously, the generator and the prediction model continuously compete against each other during the learning process, which enhances the model's generalization ability and prediction accuracy. Experimental results show that the accuracy of our proposed method reaches 94.68% and 98.22% in 5-fold cross-validation on yeast and human datasets, respectively. These results either surpass or closely approach the performance of advanced PPI prediction models such as PIPR, convolutional neural network, DeepTrio, and DeepFE, indicating that the method proposed in this article provides an effective solution for the work related to PPI prediction.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"804-816"},"PeriodicalIF":1.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148205008","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

ViraFit: Tunable Fitness Model for Viral Evolution Within a Contact Network. ViraFit：接触网络中病毒进化的可调适应模型。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2026-07-01 Epub Date: 2026-05-12 DOI: 10.1177/15578666261444396

Badhan Das, Lenwood S Heath

{"title":"ViraFit: Tunable Fitness Model for Viral Evolution Within a Contact Network.","authors":"Badhan Das, Lenwood S Heath","doi":"10.1177/15578666261444396","DOIUrl":"10.1177/15578666261444396","url":null,"abstract":"The spread of a virus can be modeled as the diffusion of virion populations along the edges of a host contact network. Most prior research assumes that the virus maintains the same genome throughout the diffusion process, and, consequently, the genome itself is of no modeling interest. Eletreby et al. do consider a model of multiple variants in which a virus can mutate within a host into another variant before transmission across an edge. Still, they fail to model the genome or any established fitness models. We incorporate three biological notions, fitness landscapes, viral quasispecies, and genome structure, to more accurately model the diffusion of a virus with mutations. We investigate established fitness landscape models and various simulated contact networks. We simulate the diffusion process across the contact network, incorporating co-occurring evolutionary and epidemiological processes. In our proof-of-concept simulations, while determining which variant should infect an exposed individual, we consider the fitness values of the variants. ViraFit tunes the ruggedness of the fitness landscape by varying several attributes: genome length, network size, mutation rate, infection probability, and infection time. Our simulation results demonstrate the importance of including the fitness value of each variant and how the fitter ones persist over time.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"761-774"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147874184","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

Simulating Protein Dynamics in Cell Signaling Pathways: A Mathematical Model Approach Incorporating Negative Interaction Mechanisms. 模拟细胞信号通路中的蛋白质动力学：一种包含负相互作用机制的数学模型方法。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2026-07-01 Epub Date: 2026-04-27 DOI: 10.1177/15578666261443351

Minsoo Kim, Eunjung Kim

{"title":"Simulating Protein Dynamics in Cell Signaling Pathways: A Mathematical Model Approach Incorporating Negative Interaction Mechanisms.","authors":"Minsoo Kim, Eunjung Kim","doi":"10.1177/15578666261443351","DOIUrl":"10.1177/15578666261443351","url":null,"abstract":"This study presents an improved mathematical model that incorporates negative interaction mechanisms to predict the dynamics of cell signaling pathways. By employing stochastic differential equations and the Euler-Maruyama method, we simulate the responses of proteins within the mitogen-activated protein kinase and oxytocin signaling pathways over time. Conventional signaling models that consider only positive interactions often lead to unrealistic signal over-amplification, the absence of oscillatory dynamics, and an inability to reproduce compensatory responses following targeted inhibition. To address these limitations, our model explicitly incorporates inhibitory interactions through a sign-changing characteristic function and bounds protein activity using a hyperbolic-tangent transfer function, ensuring biologically plausible saturation behavior. Our findings indicate that the inhibition of upstream proteins such as MEK1/2 leads to a rapid decrease in ERK1/2 activation while causing a compensatory increase in other proteins such as SOS, RAS, and RAF. Furthermore, we explore the synergistic effects of combination therapies, demonstrating that targeting multiple signaling pathways can enhance therapeutic efficacy. Through the application of the Bliss Independence Index, we assess the effectiveness of these therapeutic combinations. Additionally, we investigate the effects of abnormal activation increases caused by gain-of-function mutations on downstream proteins and the resulting changes in balance induced by negative interactions. Overall, our enhanced mathematical model serves as a valuable tool for simulating signaling dynamics with inhibitory crosstalk and for generating mechanistic hypotheses relevant to targeted and combination therapies.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"744-760"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772883","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

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph. 基于生成对抗网络和知识图的非对称药物-药物相互作用预测。

IF 1.3 4区生物学

Journal of Computational Biology Pub Date : 2026-07-01 Epub Date: 2026-05-28 DOI: 10.1177/15578666261443345

Chaokun Yan, Quanao Li, Junwei Luo, Jianlin Wang, Ge Zhang, Huimin Luo

{"title":"Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.","authors":"Chaokun Yan, Quanao Li, Junwei Luo, Jianlin Wang, Ge Zhang, Huimin Luo","doi":"10.1177/15578666261443345","DOIUrl":"10.1177/15578666261443345","url":null,"abstract":"Drug-drug interaction (DDI) prediction remains a significant challenge due to the complexity of biological systems and the growing demand for precise predictions. Recent advances in deep learning have been successfully applied to DDI prediction. However, the asymmetrical nature of DDIs is always neglected, which can lead to some information loss during the feature learning process. To address the issue, a novel DDI prediction method based on knowledge graph and generative adversarial network, KGGAN-DDI, is proposed to predict potential DDIs. First, a knowledge graph embedding module is designed to capture and encode asymmetric associations between drug pairs, which can enhance the feature representation and contextual relevance of drug interactions. Then, a dual-generator GAN is adopted to produce realistic samples and improve the prediction accuracy further. Moreover, a least squares loss function is utilized to mitigate the vanishing gradient problem, thereby providing smoother gradients and enhancing the efficiency and stability of the optimization process. Extensive experiments demonstrate that the proposed KGGAN-DDI performs better than state-of-the-art methods. Case study further shows the effectiveness of KGGAN-DDI.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"717-732"},"PeriodicalIF":1.3,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148042415","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

Evaluating Protein Language Model Embeddings for Viral Clade Assignment. 评估蛋白质语言模型嵌入的病毒分支分配。

IF 1.6 4区生物学

Journal of Computational Biology Pub Date : 2026-07-01 Epub Date: 2026-04-21 DOI: 10.1177/15578666261443336

Brendonas Stakauskas, Virginijus MarcinkeviČius

引用次数: 0