Journal of Computational Biology最新文献_第2页

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 : 2025-03-01 Epub 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":"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":"316-329"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","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 : 2025-03-01 Epub 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":"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":"330-350"},"PeriodicalIF":1.4,"publicationDate":"2025-03-01","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

A New Structure Feature Introduced to Predict Protein-Protein Interaction Sites.

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-26 DOI: 10.1089/cmb.2024.0804

Lingwei Lai, Jing Geng, Haochen Duan, Siyuan Chen, Lvwen Huang, Jiantao Yu

引用次数: 0

Subject-Specific Dosage Estimation for Primary Hypothyroidism Using Sparse Data.

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-17 DOI: 10.1089/cmb.2024.0752

Devleena Ghosh, Chittaranjan Mandal

{"title":"Subject-Specific Dosage Estimation for Primary Hypothyroidism Using Sparse Data.","authors":"Devleena Ghosh, Chittaranjan Mandal","doi":"10.1089/cmb.2024.0752","DOIUrl":"https://doi.org/10.1089/cmb.2024.0752","url":null,"abstract":"Subject-specific dosage estimation for primary hypothyroidism using subject-specific parameters of the thyrotropic regulation system is presented in this work. The data needed for such personalized modeling are usually sparse. This is addressed by utilizing available data along with domain knowledge for estimation of model parameters but with some uncertainty. Optimization-based dosage estimation approaches may not be applicable in the presence of such uncertainty. In this work, the optimal drug dosage range based on estimated parameter ranges for primary hypothyroid condition is estimated using the mathematical model through satisfiability modulo theory (SMT)-based analysis. The salient features of this work are as follows: (1) estimation of subject-specific model parameters with uncertainty using subject-specific pre-treatment and post-treatment observations, (2) modeling periodic drug administration as part of the ordinary differential equation model of thyrotropic regulation pathway through Fourier series approximation, (3) application of SMT-based analysis for determining optimal dosage range using this model and estimated parameter ranges, and (4) an initial dosage estimation method using the regression model. Results have been obtained to support the working of the developed computational procedures.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143433256","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

VIPER: Virus Inhibition Via Peptide Engineering and Receptor Mimicry.

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-14 DOI: 10.1089/cmb.2024.0866

Anna Sophie Klingenberg, Dario Ghersi

引用次数: 0

Disease Spread Model in Structurally Complex Spaces: An Open Markov Chain Approach.

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-11 DOI: 10.1089/cmb.2024.0630

Brenda Ivette García-Maya, Yehtli Morales-Huerta, Raúl Salgado-García

{"title":"Disease Spread Model in Structurally Complex Spaces: An Open Markov Chain Approach.","authors":"Brenda Ivette García-Maya, Yehtli Morales-Huerta, Raúl Salgado-García","doi":"10.1089/cmb.2024.0630","DOIUrl":"https://doi.org/10.1089/cmb.2024.0630","url":null,"abstract":"Understanding the dynamical behavior of infectious disease propagation within enclosed spaces is crucial for effectively establishing control measures. In this article, we present a modeling approach to analyze the dynamics of individuals in enclosed spaces, where such spaces are comprised of different chambers. Our focus is on capturing the movement of individuals and their infection status using an open Markov chain framework. Unlike ordinary Markov chains, an open Markov chain accounts for individuals entering and leaving the system. We categorize individuals within the system into three different groups: susceptible, carrier, and infected. A discrete-time process is employed to model the behavior of individuals throughout the system. To quantify the risk of infection, we derive a probability function that takes into account the total number of individuals inside the system and the distribution among the different groups. Furthermore, we calculate mathematical expressions for the average number of susceptible, carrier, and infected individuals at each time step. Additionally, we determine mathematical expressions for the mean number and stationary mean populations of these groups. To validate our modeling approach, we compare the theoretical and numerical models proposed in this work.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391035","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

CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma. CFINet：用于胶质母细胞瘤假性进展预测的跨模态磁共振成像特征交互网络

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-01 Epub Date: 2024-07-08 DOI: 10.1089/cmb.2024.0518

Ya Lv, Jin Liu, Xu Tian, Pei Yang, Yi Pan

{"title":"CFINet: Cross-Modality MRI Feature Interaction Network for Pseudoprogression Prediction of Glioblastoma.","authors":"Ya Lv, Jin Liu, Xu Tian, Pei Yang, Yi Pan","doi":"10.1089/cmb.2024.0518","DOIUrl":"10.1089/cmb.2024.0518","url":null,"abstract":"Pseudoprogression (PSP) is a related reaction of glioblastoma treatment, and misdiagnosis can lead to unnecessary intervention. Magnetic resonance imaging (MRI) provides cross-modality images for PSP prediction studies. However, how to effectively use the complementary information between the cross-modality MRI to improve PSP prediction is still a challenging task. To address this challenge, we propose a cross-modality feature interaction network for PSP prediction. Firstly, we propose a triple-branch multi-scale module to extract low-order feature representations and a skip-connection multi-scale module to extract high-order feature representations. Then, a cross-modality interaction module based on attention mechanism is designed to make the complementary information between cross-modality MRI fully interact. Finally, the high-order cross-modality interaction information is fed into a multi-layer perceptron to achieve the PSP prediction task. We evaluate the proposed network on a private dataset with 52 subjects from Hunan Cancer Hospital and validate it on a private dataset with 30 subjects from Xiangya Hospital. The accuracy of our proposed network on the datasets is 0.954 and 0.929, respectively, which is better than most typical convolutional neural network and interaction methods.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"212-224"},"PeriodicalIF":1.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141554866","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

BiRNN-DDI: A Drug-Drug Interaction Event Type Prediction Model Based on Bidirectional Recurrent Neural Network and Graph2Seq Representation. BiRNN-DDI：基于双向循环神经网络和 Graph2Seq 表示的药物-药物相互作用事件类型预测模型。

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-01 Epub Date: 2024-07-25 DOI: 10.1089/cmb.2024.0476

GuiShen Wang, Hui Feng, Chen Cao

引用次数: 0

Pathway Realizability in Chemical Networks.

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.2024.0521

Jakob L Andersen, Sissel Banke, Rolf Fagerberg, Christoph Flamm, Daniel Merkle, Peter F Stadler

引用次数: 0

Estimating Enzyme Expression and Metabolic Pathway Activity in Borreliella-Infected and Uninfected Mice. 估计感染博雷利杆菌和未感染博雷利杆菌小鼠的酶表达和代谢途径活性

IF 1.4 4区生物学

Journal of Computational Biology Pub Date : 2025-02-01 Epub Date: 2024-06-27 DOI: 10.1089/cmb.2024.0564

Filipp Martin Rondel, Hafsa Farooq, Roya Hosseini, Akshay Juyal, Sergey Knyazev, Serghei Mangul, Artem S Rogovskyy, Alexander Zelikovsky

{"title":"Estimating Enzyme Expression and Metabolic Pathway Activity in Borreliella-Infected and Uninfected Mice.","authors":"Filipp Martin Rondel, Hafsa Farooq, Roya Hosseini, Akshay Juyal, Sergey Knyazev, Serghei Mangul, Artem S Rogovskyy, Alexander Zelikovsky","doi":"10.1089/cmb.2024.0564","DOIUrl":"10.1089/cmb.2024.0564","url":null,"abstract":"Evaluating changes in metabolic pathway activity is essential for studying disease mechanisms and developing new treatments, with significant benefits extending to human health. Here, we propose EMPathways2, a maximum likelihood pipeline that is based on the expectation-maximization algorithm, which is capable of evaluating enzyme expression and metabolic pathway activity level. We first estimate enzyme expression from RNA-seq data that is used for simultaneous estimation of pathway activity levels using enzyme participation levels in each pathway. We implement the novel pipeline to RNA-seq data from several groups of mice, which provides a deeper look at the biochemical changes occurring as a result of bacterial infection, disease, and immune response. Our results show that estimated enzyme expression, pathway activity levels, and enzyme participation levels in each pathway are robust and stable across all samples. Estimated activity levels of a significant number of metabolic pathways strongly correlate with the infected and uninfected status of the respective rodent types.","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"188-197"},"PeriodicalIF":1.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141457117","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