IEEE/ACM Transactions on Computational Biology and Bioinformatics最新文献_第7页

AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes. AirLift：快速、全面的参考基因组间重配技术。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-19 DOI: 10.1109/TCBB.2024.3433378

Jeremie S Kim, Can Firtina, Meryem Banu Cavlak, Damla Senol Cali, Nastaran Hajinazar, Mohammed Alser, Can Alkan, Onur Mutlu

引用次数: 0

TeaTFactor: A Prediction Tool for Tea Plant Transcription Factors Based on BERT TeaTFactor：基于BERT的茶树转录因子预测工具。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-16 DOI: 10.1109/TCBB.2024.3444466

Qinan Tang;Ying Xiang;Wanling Gao;Liqiang Zhu;Zishu Xu;Yeyun Li;Zhenyu Yue

{"title":"TeaTFactor: A Prediction Tool for Tea Plant Transcription Factors Based on BERT","authors":"Qinan Tang;Ying Xiang;Wanling Gao;Liqiang Zhu;Zishu Xu;Yeyun Li;Zhenyu Yue","doi":"10.1109/TCBB.2024.3444466","DOIUrl":"10.1109/TCBB.2024.3444466","url":null,"abstract":"A transcription factor (TF) is a sequence-specific DNA-binding protein, which plays key roles in cell-fate decision by regulating gene expression. Predicting TFs is key for tea plant research community, as they regulate gene expression, influencing plant growth, development, and stress responses. It is a challenging task through wet lab experimental validation, due to their rarity, as well as the high cost and time requirements. As a result, computational methods are increasingly popular to be chosen. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved impressive performance. In this paper, we present a novel recognition algorithm named TeaTFactor that utilizes pre-training for the model training of TFs prediction. The model is built upon the BERT architecture, initially pre-trained using protein data from UniProt. Subsequently, the model was fine-tuned using the collected TFs data of tea plants. We evaluated four different word segmentation methods and the existing state-of-the-art prediction tools. According to the comprehensive experimental results and a case study, our model is superior to existing models and achieves the goal of accurate identification. In addition, we have developed a web server at \u0000<uri>http://teatfactor.tlds.cc</uri>\u0000, which we believe will facilitate future studies on tea transcription factors and advance the field of crop synthetic biology.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2123-2132"},"PeriodicalIF":3.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992290","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

Joint Extraction of Biomedical Events Based on Dynamic Path Planning Strategy and Hybrid Neural Network 基于动态路径规划策略和混合神经网络的生物医学事件联合提取。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-13 DOI: 10.1109/TCBB.2024.3442199

Xinyu He;Yujie Tang;Bo Yu;Shixin Li;Yonggong Ren

引用次数: 0

Deep Learning in Gene Regulatory Network Inference: A Survey 基因调控网络推断中的深度学习：调查。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-13 DOI: 10.1109/TCBB.2024.3442536

Jiayi Dong;Jiahao Li;Fei Wang

{"title":"Deep Learning in Gene Regulatory Network Inference: A Survey","authors":"Jiayi Dong;Jiahao Li;Fei Wang","doi":"10.1109/TCBB.2024.3442536","DOIUrl":"10.1109/TCBB.2024.3442536","url":null,"abstract":"Understanding the intricate regulatory relationships among genes is crucial for comprehending the development, differentiation, and cellular response in living systems. Consequently, inferring gene regulatory networks (GRNs) based on observed data has gained significant attention as a fundamental goal in biological applications. The proliferation and diversification of available data present both opportunities and challenges in accurately inferring GRNs. Deep learning, a highly successful technique in various domains, holds promise in aiding GRN inference. Several GRN inference methods employing deep learning models have been proposed; however, the selection of an appropriate method remains a challenge for life scientists. In this survey, we provide a comprehensive analysis of 12 GRN inference methods that leverage deep learning models. We trace the evolution of these major methods and categorize them based on the types of applicable data. We delve into the core concepts and specific steps of each method, offering a detailed evaluation of their effectiveness and scalability across different scenarios. These insights enable us to make informed recommendations. Moreover, we explore the challenges faced by GRN inference methods utilizing deep learning and discuss future directions, providing valuable suggestions for the advancement of data scientists in this field.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2089-2101"},"PeriodicalIF":3.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975613","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

Constrained Pseudo-Time Ordering for Clinical Transcriptomics Data 临床转录组学数据的受限伪时间排序

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-13 DOI: 10.1109/TCBB.2024.3442669

Sachin Mathur;Hamid Mattoo;Ziv Bar-Joseph

{"title":"Constrained Pseudo-Time Ordering for Clinical Transcriptomics Data","authors":"Sachin Mathur;Hamid Mattoo;Ziv Bar-Joseph","doi":"10.1109/TCBB.2024.3442669","DOIUrl":"10.1109/TCBB.2024.3442669","url":null,"abstract":"Time series RNASeq studies can enable understanding of the dynamics of disease progression and treatment response in patients. They also provide information on biomarkers, activated and repressed pathways, and more. While useful, data from multiple patients is challenging to integrate due to the heterogeneity in treatment response among patients, and the small number of timepoints that are usually profiled. Due to the heterogeneity among patients, relying on the sampled time points to integrate data across individuals is challenging and does not lead to correct reconstruction of the response patterns. To address these challenges, we developed a new constrained based pseudo-time ordering method for analyzing transcriptomics data in clinical and response studies. Our method allows the assignment of samples to their correct placement on the response curve while respecting the individual patient order. We use polynomials to represent gene expression over the duration of the study and an EM algorithm to determine parameters and locations. Application to four treatment response datasets shows that our method improves on prior methods and leads to accurate orderings that provide new biological insight on the disease and response.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2076-2088"},"PeriodicalIF":3.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975612","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

AGML: Adaptive Graph-Based Multi-Label Learning for Prediction of RBP and as Event Associations During EMT AGML：基于图形的自适应多标签学习，用于预测 EMT 期间的 RBP 和 AS 事件关联。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-12 DOI: 10.1109/TCBB.2024.3440913

Yushan Qiu;Wensheng Chen;Wai-Ki Ching;Hongmin Cai;Hao Jiang;Quan Zou

{"title":"AGML: Adaptive Graph-Based Multi-Label Learning for Prediction of RBP and as Event Associations During EMT","authors":"Yushan Qiu;Wensheng Chen;Wai-Ki Ching;Hongmin Cai;Hao Jiang;Quan Zou","doi":"10.1109/TCBB.2024.3440913","DOIUrl":"10.1109/TCBB.2024.3440913","url":null,"abstract":"Increasing evidence has indicated that RNA-binding proteins (RBPs) play an essential role in mediating alternative splicing (AS) events during epithelial-mesenchymal transition (EMT). However, due to the substantial cost and complexity of biological experiments, how AS events are regulated and influenced remains largely unknown. Thus, it is important to construct effective models for inferring hidden RBP-AS event associations during EMT process. In this paper, a novel and efficient model was developed to identify AS event-related candidate RBPs based on Adaptive Graph-based Multi-Label learning (AGML). In particular, we propose to adaptively learn a new affinity graph to capture the intrinsic structure of data for both RBPs and AS events. Multi-view similarity matrices are employed for maintaining the intrinsic structure and guiding the adaptive graph learning. We then simultaneously update the RBP and AS event associations that are predicted from both spaces by applying multi-label learning. The experimental results have shown that our AGML achieved AUC values of 0.9521 and 0.9873 by 5-fold and leave-one-out cross-validations, respectively, indicating the superiority and effectiveness of our proposed model. Furthermore, AGML can serve as an efficient and reliable tool for uncovering novel AS events-associated RBPs and is applicable for predicting the associations between other biological entities.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2113-2122"},"PeriodicalIF":3.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141971037","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

Editorial Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare 编辑本段深度学习驱动的生物医学应用和数字医疗大数据分析

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-08 DOI: 10.1109/TCBB.2024.3371808

Xiaokang Zhou;Carson K. Leung;Kevin I-Kai Wang;Giancarlo Fortino

{"title":"Editorial Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare","authors":"Xiaokang Zhou;Carson K. Leung;Kevin I-Kai Wang;Giancarlo Fortino","doi":"10.1109/TCBB.2024.3371808","DOIUrl":"https://doi.org/10.1109/TCBB.2024.3371808","url":null,"abstract":"Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of artificial intelligence (AI) and Internets of Things (IoT) technologies, deep learning (DL) for big data analytics—including affective learning, reinforcement learning, and transfer learning—are widely applied to sense, learn, and interact with human health. Examples of biomedical applications include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, DL and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, DL, and big data analytic technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and DL technologies. Hence, exploring the possibility of DL and big data analytics in the fields of biomedical applications and digital healthcare is in high demand.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 4","pages":"516-520"},"PeriodicalIF":3.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10631783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965894","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

DMAMP: A Deep-Learning Model for Detecting Antimicrobial Peptides and Their Multi-Activities DMAMP：用于检测抗菌肽及其多重活性的深度学习模型。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-06 DOI: 10.1109/TCBB.2024.3439541

Qiaozhen Meng;Genlang Chen;Bin Lin;Shixin Zheng;Yulai Lin;Jijun Tang;Fei Guo

{"title":"DMAMP: A Deep-Learning Model for Detecting Antimicrobial Peptides and Their Multi-Activities","authors":"Qiaozhen Meng;Genlang Chen;Bin Lin;Shixin Zheng;Yulai Lin;Jijun Tang;Fei Guo","doi":"10.1109/TCBB.2024.3439541","DOIUrl":"10.1109/TCBB.2024.3439541","url":null,"abstract":"Due to the broad-spectrum and high-efficiency antibacterial activity, antimicrobial peptides (AMPs) and their functions have been studied in the field of drug discovery. Using biological experiments to detect the AMPs and corresponding activities require a high cost, whereas computational technologies do so for much less. Currently, most computational methods solve the identification of AMPs and their activities as two independent tasks, which ignore the relationship between them. Therefore, the combination and sharing of patterns for two tasks is a crucial problem that needs to be addressed. In this study, we propose a deep learning model, called DMAMP, for detecting AMPs and activities simultaneously, which is benefited from multi-task learning. The first stage is to utilize convolutional neural network models and residual blocks to extract the sharing hidden features from two related tasks. The next stage is to use two fully connected layers to learn the distinct information of two tasks. Meanwhile, the original evolutionary features from the peptide sequence are also fed to the predictor of the second task to complement the forgotten information. The experiments on the independent test dataset demonstrate that our method performs better than the single-task model with 4.28% of Matthews Correlation Coefficient (MCC) on the first task, and achieves 0.2627 of an average MCC which is higher than the single-task model and two existing methods for five activities on the second task. To understand whether features derived from the convolutional layers of models capture the differences between target classes, we visualize these high-dimensional features by projecting into 3D space. In addition, we show that our predictor has the ability to identify peptides that achieve activity against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). We hope that our proposed method can give new insights into the discovery of novel antiviral peptide drugs.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2025-2034"},"PeriodicalIF":3.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897345","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

Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning Hyb_SEnc：基于混合特征向量和堆叠集合学习的抗结核肽预测器

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-31 DOI: 10.1109/TCBB.2024.3425644

Xiuhao Fu;Hao Duan;Xiaofeng Zang;Chunling Liu;Xingfeng Li;Qingchen Zhang;Zilong Zhang;Quan Zou;Feifei Cui

{"title":"Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning","authors":"Xiuhao Fu;Hao Duan;Xiaofeng Zang;Chunling Liu;Xingfeng Li;Qingchen Zhang;Zilong Zhang;Quan Zou;Feifei Cui","doi":"10.1109/TCBB.2024.3425644","DOIUrl":"10.1109/TCBB.2024.3425644","url":null,"abstract":"Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The rise of peptide drugs has created a new direction in the treatment of tuberculosis. Therefore, for the treatment of tuberculosis, the prediction of anti-tuberculosis peptides is crucial. This paper proposes an anti-tuberculosis peptide prediction method based on hybrid features and stacked ensemble learning. First, a random forest (RF) and extremely randomized tree (ERT) are selected as first-level learning of stacked ensembles. Then, the five best-performing feature encoding methods are selected to obtain the hybrid feature vector, and then the decision tree and recursive feature elimination (DT-RFE) are used to refine the hybrid feature vector. After selection, the optimal feature subset is used as the input of the stacked ensemble model. At the same time, logistic regression (LR) is used as a stacked ensemble secondary learner to build the final stacked ensemble model Hyb_SEnc. The prediction accuracy of Hyb_SEnc achieved 94.68% and 95.74% on the independent test sets of AntiTb_MD and AntiTb_RD, respectively.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"1897-1910"},"PeriodicalIF":3.6,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859585","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

KGRLFF: Detecting Drug-Drug Interactions Based on Knowledge Graph Representation Learning and Feature Fusion KGRLFF：基于知识图谱表示学习和特征融合的药物相互作用检测。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-07-29 DOI: 10.1109/TCBB.2024.3434992

Xiaoli Lin;Zhuang Yin;Xiaolong Zhang;Jing Hu

{"title":"KGRLFF: Detecting Drug-Drug Interactions Based on Knowledge Graph Representation Learning and Feature Fusion","authors":"Xiaoli Lin;Zhuang Yin;Xiaolong Zhang;Jing Hu","doi":"10.1109/TCBB.2024.3434992","DOIUrl":"10.1109/TCBB.2024.3434992","url":null,"abstract":"Accurate prediction of drug-drug interactions (DDIs) plays an important role in improving the efficiency of drug development and ensuring the safety of combination therapy. Most existing models rely on a single source of information to predict DDIs, and few models can perform tasks on biomedical knowledge graphs. This paper proposes a new hybrid method, namely Knowledge Graph Representation Learning and Feature Fusion (KGRLFF), to fully exploit the information from the biomedical knowledge graph and molecular structure of drugs to better predict DDIs. KGRLFF first uses a Bidirectional Random Walk sampling method based on the PageRank algorithm (BRWP) to obtain higher-order neighborhood information of drugs in the knowledge graph, including neighboring nodes, semantic relations, and higher-order information associated with triple facts. Then, an embedded representation learning model named Knowledge Graph-based Cyclic Recursive Aggregation (KGCRA) is used to learn the embedded representations of drugs by recursively propagating and aggregating messages with drugs as both the source and destination. In addition, the model learns the molecular structures of the drugs to obtain the structured features. Finally, a Feature Representation Fusion Strategy (FRFS) was developed to integrate embedded representations and structured feature representations. Experimental results showed that KGRLFF is feasible for predicting potential DDIs.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2035-2049"},"PeriodicalIF":3.6,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10613488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792317","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