Journal of Computational Biology最新文献_第2页

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

Generative Adversarial Networks for Neuroimage Translation. 神经图像翻译的生成对抗网络。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-27 DOI: 10.1089/cmb.2024.0635

Cassandra Czobit, Reza Samavi

{"title":"Generative Adversarial Networks for Neuroimage Translation.","authors":"Cassandra Czobit, Reza Samavi","doi":"10.1089/cmb.2024.0635","DOIUrl":"https://doi.org/10.1089/cmb.2024.0635","url":null,"abstract":"Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class are limited. From the learning perspective, this process contributes to the data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features. In the case of generating additional neuroimages, it is advantageous to obtain unidentifiable medical data and augment smaller annotated datasets. This study proposes the development of a cycle-consistent generative adversarial network (CycleGAN) model for translating neuroimages from one field strength to another (e.g., 3 Tesla [T] to 1.5 T). This model was compared with a model based on a deep convolutional GAN model architecture. CycleGAN was able to generate the synthetic and reconstructed images with reasonable accuracy. The mapping function from the source (3 T) to the target domain (1.5 T) performed optimally with an average peak signal-to-noise ratio value of 25.69 ± 2.49 dB and a mean absolute error value of 2106.27 ± 1218.37. The codes for this study have been made publicly available in the following GitHub repository.a.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142894857","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 II 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 : 2024-12-18 DOI: 10.1089/cmb.2024.0905

Murray Patterson

引用次数: 0

Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics. 基于模糊识别的过渡细胞推断单细胞转录组学的细胞轨迹。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-13 DOI: 10.1089/cmb.2023.0432

Xiang Chen, Yibing Ma, Yongle Shi, Bai Zhang, Hanwen Wu, Jie Gao

{"title":"Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics.","authors":"Xiang Chen, Yibing Ma, Yongle Shi, Bai Zhang, Hanwen Wu, Jie Gao","doi":"10.1089/cmb.2023.0432","DOIUrl":"https://doi.org/10.1089/cmb.2023.0432","url":null,"abstract":"With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computational methods. Trajectory inference is a crucial downstream analytical task that provides valuable insights into understanding cell cycle and differentiation. During cell development, cells exhibit both stable and transition states, which makes it challenging to accurately identify these cells. To address this challenge, we propose a novel single-cell trajectory inference method using fuzzy clustering, named scFCTI. By introducing fuzzy clustering and quantifying cell uncertainty, scFCTI can identify transition cells within unstable cell states. Moreover, scFCTI can obtain refined cell classification by characterizing different cell stages, which gain more accurate single-cell trajectory reconstruction containing transition paths. To validate the effectiveness of scFCTI, we conduct experiments on five real datasets and four different structure simulation datasets, comparing them with several state-of-the-art trajectory inference methods. The results demonstrate that scFCTI outperforms these methods by successfully identifying unstable cell clusters and obtaining more accurate cell paths with transition states. Especially the experimental results demonstrate that scFCTI can reconstruct the cell trajectory more precisely.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818363","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 Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes. 基于图的机器学习方法结合光学测量来理解心肌细胞的跳动动力学。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-09 DOI: 10.1089/cmb.2024.0491

Ziqian Wu, Jiyoon Park, Paul R Steiner, Bo Zhu, John X J Zhang

引用次数: 0

Drug Repurposing Using Hypergraph Embedding Based on Common Therapeutic Targets of a Drug. 基于药物共同治疗靶点的超图嵌入药物再利用。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-09 DOI: 10.1089/cmb.2023.0427

Hanieh Abbasi, Amir Lakizadeh

{"title":"Drug Repurposing Using Hypergraph Embedding Based on Common Therapeutic Targets of a Drug.","authors":"Hanieh Abbasi, Amir Lakizadeh","doi":"10.1089/cmb.2023.0427","DOIUrl":"https://doi.org/10.1089/cmb.2023.0427","url":null,"abstract":"Developing a new drug is a long and expensive process that typically takes 10-15 years and costs billions of dollars. This has led to an increasing interest in drug repositioning, which involves finding new therapeutic uses for existing drugs. Computational methods become an increasingly important tool for identifying associations between drugs and new diseases. Graph- and hypergraph-based approaches are a type of computational method that can be used to identify potential associations between drugs and new diseases. Here, we present a drug repurposing method based on hypergraph neural network for predicting drug-disease association in three stages. First, it constructs a heterogeneous graph that contains drug and disease nodes and links between them; in the second stage, it converts the heterogeneous simple graph to a hypergraph with only disease nodes. This is achieved by grouping diseases that use the same drug into a hyperedge. Indeed, all the diseases that are the common therapeutic goal of a drug are placed on a hyperedge. Finally, a graph neural network is used to predict drug-disease association based on the structure of the hypergraph. This model is more efficient than other methods because it uses a hypergraph to model relationships more effectively than graphs. Furthermore, it constructs the hypergraph using only a drug-disease association matrix, eliminating the need for extensive amounts of data. Experimental results show that the hypergraph-based approach effectively captures complex interrelationships between drugs and diseases, leading to improved accuracy of drug-disease association prediction compared to state-of-the-art methods.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794721","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

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network. 基于扩散的图注意网络预测药物反应。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-06 DOI: 10.1089/cmb.2024.0807

Emre Sefer

{"title":"DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.","authors":"Emre Sefer","doi":"10.1089/cmb.2024.0807","DOIUrl":"https://doi.org/10.1089/cmb.2024.0807","url":null,"abstract":"Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have been a key driver of research in this area. However, these biological datasets, which are typically high dimensional but have small sample sizes, present challenges such as overfitting and poor generalization in predictive models. As a complicating matter, gene expression (GE) data must capture complex inter-gene relationships, exacerbating these issues. In this article, we tackle these challenges by introducing a drug response prediction method, called drug response graph attention network (DRGAT), which combines a denoising diffusion implicit model for data augmentation with a recently introduced graph attention network (GAT) with high-order neighbor propagation (HO-GATs) prediction module. Our proposed approach achieved almost 5% improvement in the area under receiver operating characteristic curve compared with state-of-the-art models for the many studied drugs, indicating our method's reasonable generalization capabilities. Moreover, our experiments confirm the potential of diffusion-based generative models, a core component of our method, to mitigate the inherent limitations of omics datasets by effectively augmenting GE data.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785889","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

An Earth Mover's Distance-Based Self-Supervised Framework for Cellular Dynamic Grading in Live-Cell Imaging. 在活细胞成像中，基于距离的土工自监督框架用于细胞动态分级。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-02 DOI: 10.1089/cmb.2024.0672

Fengqian Pang, Chunyue Lei, Hongfei Zhao, Zhiqiang Xing

{"title":"An Earth Mover's Distance-Based Self-Supervised Framework for Cellular Dynamic Grading in Live-Cell Imaging.","authors":"Fengqian Pang, Chunyue Lei, Hongfei Zhao, Zhiqiang Xing","doi":"10.1089/cmb.2024.0672","DOIUrl":"https://doi.org/10.1089/cmb.2024.0672","url":null,"abstract":"Cellular appearance and its dynamics frequently serve as a proxy measurement of live-cell physiological properties. The computational analysis of cell properties is considered to be a significant endeavor in biological and biomedical research. Deep learning has garnered considerable success across various fields. In light of this, various neural networks have been developed to analyze live-cell microscopic videos and capture cellular dynamics with biological significance. Specifically, cellular dynamic grading (CDG) is the task that provides a predefined dynamic grade for a live-cell according to the speed of cellular deformation and intracellular movement. This task involves recording the morphological and cytoplasmic dynamics in live-cell microscopic videos. Similar to other medical image processing tasks, CDG faces challenges in collecting and annotating cellular videos. These deficiencies in medical data limit the performance of deep learning models. In this article, we propose a novel self-supervised framework to overcome these limitations for the CDG task. Our framework relies on the assumption that increasing or decreasing cell dynamic grades is consistent with accelerating or decelerating cell appearance change in videos, respectively. This consistency is subsequently incorporated as a constraint in the loss function for the self-supervised training strategy. Our framework is implemented by formulating a probability transition matrix based on the Earth Mover's Distance and imposing a loss constraint on the elements of this matrix. Experimental results demonstrate that our proposed framework enhances the model's ability to learn spatiotemporal dynamics. Furthermore, our framework outperforms the existing methods on our cell video database.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769460","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

High-Accuracy Positivity-Preserving Finite Difference Approximations of the Chemotaxis Model for Tumor Invasion. 肿瘤侵袭趋化模型的高精度正性保留有限差分近似值

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-01 Epub Date: 2024-10-07 DOI: 10.1089/cmb.2023.0316

Lin Zhang, Jigen Peng, Yongbin Ge, Haiyang Li, Yuchao Tang

{"title":"High-Accuracy Positivity-Preserving Finite Difference Approximations of the Chemotaxis Model for Tumor Invasion.","authors":"Lin Zhang, Jigen Peng, Yongbin Ge, Haiyang Li, Yuchao Tang","doi":"10.1089/cmb.2023.0316","DOIUrl":"10.1089/cmb.2023.0316","url":null,"abstract":"Numerical simulation of the complex evolution process for tumor invasion plays an extremely important role in-depth exploring the bio-taxis phenomena of tumor growth and metastasis. In view of the fact that low-accuracy numerical methods often have large errors and low resolution, very refined grids have to be used if we want to get high-resolution simulating results, which leads to a great deal of computational cost. In this paper, we are committed to developing a class of high-accuracy positivity-preserving finite difference methods to solve the chemotaxis model for tumor invasion. First, two unconditionally stable implicit compact difference schemes for solving the model are proposed; second, the local truncation errors of the new schemes are analyzed, which show that they have second-order accuracy in time and fourth-order accuracy in space; third, based on the proposed schemes, the high-accuracy numerical integration idea of binary functions is employed to structure a linear compact weighting formula that guarantees fourth-order accuracy and nonnegative, and then a positivity-preserving and time-marching algorithm is established; and finally, the accuracy, stability, and positivity-preserving of the proposed methods are verified by several numerical experiments, and the evolution phenomena of tumor invasion over time are numerically simulated and analyzed.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1224-1258"},"PeriodicalIF":1.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380968","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

MMG4: Recognition of G4-Forming Sequences Based on Markov Model. MMG4：基于马尔可夫模型的 G4 形成序列识别。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2024-12-01 Epub Date: 2024-10-17 DOI: 10.1089/cmb.2024.0523

Boyuan Yu, Hao Zhang, Cong Pian, Yuanyuan Chen

{"title":"MMG4: Recognition of G4-Forming Sequences Based on Markov Model.","authors":"Boyuan Yu, Hao Zhang, Cong Pian, Yuanyuan Chen","doi":"10.1089/cmb.2024.0523","DOIUrl":"10.1089/cmb.2024.0523","url":null,"abstract":"G-quadruplexes (G4s) are special nucleic acid structures with various important biological functions. Existing tools and technologies for G4-forming sequences recognition are limited to time-consuming and costly methods such as circular dichroism and nuclear magnetic resonance. Developing a fast and accurate model for G4-forming sequences recognition has far-reaching significance. In this study, MMG4, a novel model to recognize G4-forming sequences based on Markov model (MM), was developed and the phenomenon of high recognition accuracy in the central region of the sequence and low accuracy in the two end regions was discovered. It was further found that the differences in base transfer probabilities, ratio distribution, and G4-motif structural content in different regions may be the causes of this phenomenon. The study also explored the impact of sequence length on recognition accuracy and found the optimal recognition interval to be [910-1049], with the highest recognition accuracy reaching 85.95%. By extracting sequence features, the study constructed three types of machine learning models: random forest (RF), support vector machine, and back-propagation neural network. It was found that recognition performance of MM was significantly better than that of the other three machine learning models, proving that the recognition method based on MM can effectively capture the correlation information between adjacent nucleotides of G4. By combining MM with the three machine learning models, the predictive performance of MMG4 improved. Among them, the RF model combined with MM has the best performance, achieving an area under the receiver operating characteristic curve value of 0.93 and an area under the precision-recall curve value of 0.9. Finally, the study validated the model robustness and generalization ability through independent testing dataset.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"1211-1223"},"PeriodicalIF":1.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466641","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