Journal of Computational Biology最新文献_第3页

PDFll: Predictors of Disorder and Function of Proteins from the Language of Life. PDFll：从生命语言中预测蛋白质的紊乱和功能

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-01 Epub Date: 2024-09-09 DOI: 10.1089/cmb.2024.0506

Wanyi Yang, Qingsong Du, Xunyu Zhou, Chuanfang Wu, Jinku Bao

引用次数: 0

Generative AI Models for the Protein Scaffold Filling Problem. 蛋白质支架填充问题的人工智能生成模型。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-01 Epub Date: 2024-10-23 DOI: 10.1089/cmb.2024.0510

Letu Qingge, Kushal Badal, Richard Annan, Jordan Sturtz, Xiaowen Liu, Binhai Zhu

{"title":"Generative AI Models for the Protein Scaffold Filling Problem.","authors":"Letu Qingge, Kushal Badal, Richard Annan, Jordan Sturtz, Xiaowen Liu, Binhai Zhu","doi":"10.1089/cmb.2024.0510","DOIUrl":"10.1089/cmb.2024.0510","url":null,"abstract":"De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches used in the field of mass spectrometry to analyze and sequence proteins. However, these approaches often produce incomplete protein sequences with gaps, namely scaffolds. The protein scaffold filling problem refers to filling the missing amino acids in the gaps of a scaffold to infer the complete protein sequence. In this article, we tackle the protein scaffold filling problem based on generative AI techniques, such as convolutional denoising autoencoder, transformer, and generative pretrained transformer (GPT) models, to complete the protein sequences and compare our results with recently developed convolutional long short-term memory-based sequence model. We evaluate the model performance both on a real dataset and generated datasets. All proposed models show outstanding prediction accuracy. Notably, the GPT-2 model achieves 100% gap-filling accuracy and 100% full sequence accuracy on the MabCampth protein scaffold, which outperforms the other models.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"127-142"},"PeriodicalIF":1.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142501311","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

AFMDD: Analyzing Functional Connectivity Feature of Major Depressive Disorder by Graph Neural Network-Based Model.

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-01 Epub Date: 2025-02-03 DOI: 10.1089/cmb.2024.0505

Yan Zhang, Xin Liu, Panrui Tang, Zuping Zhang

{"title":"AFMDD: Analyzing Functional Connectivity Feature of Major Depressive Disorder by Graph Neural Network-Based Model.","authors":"Yan Zhang, Xin Liu, Panrui Tang, Zuping Zhang","doi":"10.1089/cmb.2024.0505","DOIUrl":"10.1089/cmb.2024.0505","url":null,"abstract":"The extraction of biomarkers from functional connectivity (FC) in the brain is of great significance for the diagnosis of mental disorders. In recent years, with the development of deep learning, several methods have been proposed to assist in the diagnosis of depression and promote its automatic identification. However, these methods still have some limitations. The current approaches overlook the importance of subgraphs in brain graphs, resulting in low accuracy. Using these methods with low accuracy for FC analysis may lead to unreliable results. To address these issues, we have designed a graph neural network-based model called AFMDD, specifically for analyzing FC features of depression and depression identification. Through experimental validation, our model has demonstrated excellent performance in depression diagnosis, achieving an accuracy of 73.15%, surpassing many state-of-the-art methods. In our study, we conducted visual analysis of nodes and edges in the FC networks of depression and identified several novel FC features. Those findings may provide valuable clues for the development of biomarkers for the clinical diagnosis of depression.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"156-163"},"PeriodicalIF":1.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080215","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

Special Issue, Part 2 19th International Symposium on Bioinformatics Research and Applications (ISBRA 2023). 特刊，第 19 届生物信息学研究与应用国际研讨会（ISBRA 2023）第二部分。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-01 Epub Date: 2024-12-18 DOI: 10.1089/cmb.2024.0905

Murray Patterson

引用次数: 0

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