IEEE/ACM Transactions on Computational Biology and Bioinformatics最新文献_第2页

ESGC-MDA: Identifying miRNA-Disease Associations Using Enhanced Simple Graph Convolutional Networks. ESGC-MDA：利用增强型简单图卷积网络识别 miRNA 与疾病的关联。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-03-01 DOI: 10.1109/TCBB.2024.3486911

Xuehua Bi, Chunyang Jiang, Cheng Yan, Kai Zhao, Linlin Zhang, Jianxin Wang

{"title":"ESGC-MDA: Identifying miRNA-Disease Associations Using Enhanced Simple Graph Convolutional Networks.","authors":"Xuehua Bi, Chunyang Jiang, Cheng Yan, Kai Zhao, Linlin Zhang, Jianxin Wang","doi":"10.1109/TCBB.2024.3486911","DOIUrl":"10.1109/TCBB.2024.3486911","url":null,"abstract":"MiRNAs play an important role in the occurrence and development of human disease. Identifying potential miRNA-disease associations is valuable for disease diagnosis and treatment. Therefore, it is urgent to develop efficient computational methods for predicting potential miRNA-disease associations to reduce the cost and time associated with biological wet experiments. In addition, high-quality feature representation remains a challenge for miRNA-disease association prediction using graph neural network methods. In this paper, we propose a method named ESGC-MDA, which employs an enhanced Simple Graph Convolution Network to identify miRNA-disease associations. We first construct a bipartite attributed graph for miRNAs and diseases by computing multi-source similarity. Then, we enhance the feature representations of miRNA and disease nodes by applying two strategies in the simple convolution network, which include randomly dropping messages during propagation to ensure the model learns more reliable feature representations, and using adaptive weighting to aggregate features from different layers. Finally, we calculate the prediction scores of miRNA-disease pairs by using a fully connected neural network decoder. We conduct 5-fold cross-validation and 10-fold cross-validation on HDMM v2.0 and HMDD v3.2, respectively, and ESGC-MDA achieves better performance than state-of-the-art baseline methods. The case studies for cardiovascular disease, lung cancer and colon cancer also further confirm the effectiveness of ESGC-MDA.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"422-432"},"PeriodicalIF":3.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142521814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey. 基于人工智能的计算方法在早期药物发现和上市后药物评估中的应用：调查。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3492708

Flora Rajaei, Cristian Minoccheri, Emily Wittrup, Richard C Wilson, Brian D Athey, Gilbert S Omenn, Kayvan Najarian

引用次数: 0

Performance Comparison Between Deep Neural Network and Machine Learning Based Classifiers for Huntington Disease Prediction From Human DNA Sequence. 基于深度神经网络和机器学习的分类器在从人类 DNA 序列预测亨廷顿病方面的性能比较。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3493203

C Vishnuppriya, G Tamilpavai

{"title":"Performance Comparison Between Deep Neural Network and Machine Learning Based Classifiers for Huntington Disease Prediction From Human DNA Sequence.","authors":"C Vishnuppriya, G Tamilpavai","doi":"10.1109/TCBB.2024.3493203","DOIUrl":"10.1109/TCBB.2024.3493203","url":null,"abstract":"Huntington Disease (HD) is a type of neurodegenerative disorder which causes problems like psychiatric disturbances, movement problem, weight loss and problem in sleep. It needs to be addressed in earlier stage of human life. Nowadays Deep Learning (DL) based system could help physicians provide second opinion in treating patient's disease. In this work, human Deoxyribo Nucleic Acid (DNA) sequence is analyzed using Deep Neural Network (DNN) algorithm to predict the HD disease. The main objective of this work is to identify whether the human DNA is affected by HD or not. Human DNA sequences are collected from National Center for Biotechnology Information (NCBI) and synthetic human DNA data are also constructed for process. Then numerical conversion of human DNA sequence data is done by Chaos Game Representation (CGR) method. After that, numerical values of DNA data are used for feature extraction. Mean, median, standard deviation, entropy, contrast, correlation, energy and homogeneity are extracted. Additionally, the following features such as counts of adenine, thymine, guanine and cytosine are extracted from the DNA sequence data itself. The extracted features are used as input to the DNN classifier and other machine learning based classifiers such as NN (Neural Network), Support Vector Machine (SVM), Random Forest (RF) and Classification Tree with Forward Pruning (CTWFP). Six performance measures are used such as Accuracy, Sensitivity, Specificity, Precision, F1 score and Mathew Correlation Co-efficient (MCC). The study concludes DNN, NN, SVM, RF achieve 100% accuracy and CTWFP achieves accuracy of 87%.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"52-63"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Detecting Boolean Asymmetric Relationships With a Loop Counting Technique and its Implications for Analyzing Heterogeneity Within Gene Expression Datasets. 利用循环计数技术检测布尔不对称关系及其对分析基因表达数据集异质性的影响

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3487434

Haosheng Zhou, Wei Lin, Sergio R Labra, Stuart A Lipton, Jeremy A Elman, Nicholas J Schork, Aaditya V Rangan

{"title":"Detecting Boolean Asymmetric Relationships With a Loop Counting Technique and its Implications for Analyzing Heterogeneity Within Gene Expression Datasets.","authors":"Haosheng Zhou, Wei Lin, Sergio R Labra, Stuart A Lipton, Jeremy A Elman, Nicholas J Schork, Aaditya V Rangan","doi":"10.1109/TCBB.2024.3487434","DOIUrl":"10.1109/TCBB.2024.3487434","url":null,"abstract":"Many traditional methods for analyzing gene-gene relationships focus on positive and negative correlations, both of which are a kind of 'symmetric' relationship. Biclustering is one such technique that typically searches for subsets of genes exhibiting correlated expression among a subset of samples. However, genes can also exhibit 'asymmetric' relationships, such as 'if-then' relationships used in boolean circuits. In this paper we develop a very general method that can be used to detect biclusters within gene-expression data that involve subsets of genes which are enriched for these 'boolean-asymmetric' relationships (BARs). These BAR-biclusters can correspond to heterogeneity that is driven by asymmetric gene-gene interactions, e.g., reflecting regulatory effects of one gene on another, rather than more standard symmetric interactions. Unlike typical approaches that search for BARs across the entire population, BAR-biclusters can detect asymmetric interactions that only occur among a subset of samples. We apply our method to a single-cell RNA-sequencing data-set, demonstrating that the statistically-significant BAR-biclusters indeed contain additional information not present within the more traditional 'boolean-symmetric'-biclusters. For example, the BAR-biclusters involve different subsets of cells, and highlight different gene-pathways within the data-set. Moreover, by combining the boolean-asymmetric- and boolean-symmetric-signals, one can build linear classifiers which outperform those built using only traditional boolean-symmetric signals.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"27-38"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12037869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

iAnOxPep: A Machine Learning Model for the Identification of Anti-Oxidative Peptides Using Ensemble Learning. iAnOxPep：利用集合学习识别抗氧化肽的机器学习模型。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3489614

Mir Tanveerul Hassan, Hilal Tayara, Kil To Chong

{"title":"iAnOxPep: A Machine Learning Model for the Identification of Anti-Oxidative Peptides Using Ensemble Learning.","authors":"Mir Tanveerul Hassan, Hilal Tayara, Kil To Chong","doi":"10.1109/TCBB.2024.3489614","DOIUrl":"10.1109/TCBB.2024.3489614","url":null,"abstract":"Due to their safety, high activity, and plentiful sources, antioxidant peptides, particularly those produced from food, are thought to be prospective competitors to synthetic antioxidants in the fight against free radical-mediated illnesses. The lengthy and laborious trial-and-error method for identifying antioxidative peptides (AOP) has raised interest in creating computational-based methods. There exist two state-of-the-art AOP predictors; however, the restriction on peptide sequence length makes them inviable. By overcoming the aforementioned problem, a novel predictor might be useful in the context of AOP prediction. The method has been trained, tested, and evaluated on two datasets: a balanced one and an unbalanced one. We used seven different descriptors and five machine-learning (ML) classifiers to construct 35 baseline models. Five ML classifiers were further trained to create five meta-models using the combined output of 35 baseline models. Finally, these five meta-models were aggregated together through ensemble learning to create a robust predictive model named iAnOxPep. On both datasets, our proposed model demonstrated good prediction performance when compared to baseline models and meta-models, demonstrating the superiority of our approach in the identification of AOPs. For the purpose of screening and identifying possible AOPs, we anticipate that the iAnOxPep method will be an invaluable tool.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"85-96"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142619332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model. DeepLigType：使用深度学习模型预测蛋白质配体结合位点的配体类型。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3493820

Orhun Vural, Leon Jololian, Lurong Pan

{"title":"DeepLigType: Predicting Ligand Types of Protein-Ligand Binding Sites Using a Deep Learning Model.","authors":"Orhun Vural, Leon Jololian, Lurong Pan","doi":"10.1109/TCBB.2024.3493820","DOIUrl":"10.1109/TCBB.2024.3493820","url":null,"abstract":"The analysis of protein-ligand binding sites plays a crucial role in the initial stages of drug discovery. Accurately predicting the ligand types that are likely to bind to protein-ligand binding sites enables more informed decision making in drug design. Our study, DeepLigType, determines protein-ligand binding sites using Fpocket and then predicts the ligand type of these pockets with the deep learning model, Convolutional Block Attention Module (CBAM) with ResNet. CBAM-ResNet has been trained to accurately predict five distinct ligand types. We classified protein-ligand binding sites into five different categories according to the type of response ligands cause when they bind to their target proteins, which are antagonist, agonist, activator, inhibitor, and others. We created a novel dataset, referred to as LigType5, from the widely recognized PDBbind and scPDB dataset for training and testing our model. While the literature mostly focuses on the specificity and characteristic analysis of protein binding sites by experimental (laboratory-based) methods, we propose a computational method with the DeepLigType architecture. DeepLigType demonstrated an accuracy of 74.30% and an AUC of 0.83 in ligand type prediction on a novel test dataset using the CBAM-ResNet deep learning model.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"116-123"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142604213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

LHPre: Phage Host Prediction With VAE-Based Class Imbalance Correction and Lyase Sequence Embedding. LHPre：利用基于 VAE 的类不平衡校正和 Lyase 序列嵌入进行噬菌体宿主预测。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3488059

Jia Wang, Zhenjing Yu, Jianqiang Li

{"title":"LHPre: Phage Host Prediction With VAE-Based Class Imbalance Correction and Lyase Sequence Embedding.","authors":"Jia Wang, Zhenjing Yu, Jianqiang Li","doi":"10.1109/TCBB.2024.3488059","DOIUrl":"10.1109/TCBB.2024.3488059","url":null,"abstract":"The escalation of antibiotic resistance underscores the need for innovative approaches to combat bacterial infections. Phage therapy has emerged as a promising solution, wherein host determination plays an important role. Phage lysins, characterized by their specificity in targeting and cleaving corresponding host bacteria, serve as key players in this paradigm. In this study, we present a novel approach by leveraging genes of phage-encoded lytic enzymes for host prediction, culminating in the development of LHPre. Initially, gene fragments of phage-encoded lytic enzymes and their respective hosts were collected from the database. Second, DNA sequences were encoded using the Frequency Chaos Game Representation (FCGR) method, and pseudo samples were generated employing the Variational Autoencoder (VAE) model to address class imbalance. Finally, a prediction model was constructed using the Vision Transformer(Vit) model. Five-fold cross-validation results demonstrated that LHPre surpassed other state-of-the-art phage host prediction methods, achieving accuracies of 85.04%, 90.01%, and 93.39% at the species, genus, and family levels, respectively.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"PP ","pages":"73-84"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142545204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Improving Molecule Generation and Drug Discovery With a Knowledge-Enhanced Generative Model. 利用知识增强型生成模型改进分子生成和药物发现。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3477313

Aditya Malusare, Vaneet Aggarwal

引用次数: 0

Enhancing Single-Cell RNA-Seq Data Completeness With a Graph Learning Framework. 利用图形学习框架提高单细胞 RNA-seq 数据的完整性。

IF 3.4 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2025-01-01 DOI: 10.1109/TCBB.2024.3492384

Snehalika Lall, Sumanta Ray, Sanghamitra Bandyopadhyay

引用次数: 0

Guest Editorial Guest Editorial for the 20th Asia Pacific Bioinformatics Conference 第20届亚太生物信息学会议客座评论