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

Compact Class-Conditional Attribute Category Clustering: Amino Acid Grouping for Enhanced HIV-1 Protease Cleavage Classification 紧凑型类条件属性类别聚类：用于增强 HIV-1 蛋白酶裂解分类的氨基酸分组。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-23 DOI: 10.1109/TCBB.2024.3448617

José A. Sáez;J. Fernando Vera

{"title":"Compact Class-Conditional Attribute Category Clustering: Amino Acid Grouping for Enhanced HIV-1 Protease Cleavage Classification","authors":"José A. Sáez;J. Fernando Vera","doi":"10.1109/TCBB.2024.3448617","DOIUrl":"10.1109/TCBB.2024.3448617","url":null,"abstract":"Categorical attributes are common in many classification tasks, presenting certain challenges as the number of categories grows. This situation can affect data handling, negatively impacting the building time of models, their complexity and, ultimately, their classification performance. In order to mitigate these issues, this research proposes a novel preprocessing technique for grouping attribute categories in classification datasets. This approach combines the exact representation of the association between categorical values in a Euclidean space, clustering methods and attribute quality metrics to group similar attribute categories based on their contribution to the classification task. To estimate its effectiveness, the proposal is evaluated within the context of HIV-1 protease cleavage site prediction, where each attribute represents an amino acid that can take multiple possible values. The results obtained on HIV-1 real-world datasets show a significant reduction in the number of categories per attribute, with an average reduction percentage ranging from 74% to 81%. This reduction leads to simplified data representations and improved classification performances compared to not preprocessing. Specifically, improvements of up to 0.07 in accuracy and 0.19 in geometric mean are observed across different datasets and classification algorithms. Additionally, extensive simulations on synthetic datasets with varied characteristics are carried out, providing consistent and reliable results that validate the robustness of the proposal. These findings highlight the capability of the developed method to enhance cleavage prediction, which could potentially contribute to understanding viral processes and developing targeted therapeutic strategies.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2167-2178"},"PeriodicalIF":3.6,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10645313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142043936","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

A Method for Inferring Polymers Based on Linear Regression and Integer Programming 基于线性回归和整数编程的聚合物推断方法。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-22 DOI: 10.1109/TCBB.2024.3447780

Ryota Ido;Shengjuan Cao;Jianshen Zhu;Naveed Ahmed Azam;Kazuya Haraguchi;Liang Zhao;Hiroshi Nagamochi;Tatsuya Akutsu

引用次数: 0

KGRACDA: A Model Based on Knowledge Graph from Recursion and Attention Aggregation for CircRNA-Disease Association Prediction KGRACDA：基于知识图谱的递归和注意力聚合的 CircRNA-疾病关联预测模型

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-21 DOI: 10.1109/TCBB.2024.3447110

Ying Wang;Maoyuan Ma;Yanxin Xie;Qinke Peng;Hongqiang Lyu;Hequan Sun;Laiyi Fu

{"title":"KGRACDA: A Model Based on Knowledge Graph from Recursion and Attention Aggregation for CircRNA-Disease Association Prediction","authors":"Ying Wang;Maoyuan Ma;Yanxin Xie;Qinke Peng;Hongqiang Lyu;Hequan Sun;Laiyi Fu","doi":"10.1109/TCBB.2024.3447110","DOIUrl":"10.1109/TCBB.2024.3447110","url":null,"abstract":"CircRNA is closely related to human disease, so it is important to predict circRNA-disease association (CDA). However, the traditional biological detection methods have high difficulty and low accuracy, and computational methods represented by deep learning ignore the ability of the model to explicitly extract local depth information of the CDA. We propose a model based on knowledge graph from recursion and attention aggregation for circRNA-disease association prediction (KGRACDA). This model combines explicit structural features and implicit embedding information of graphs, optimizing graph embedding vectors. First, we built large-scale, multi-source heterogeneous datasets and construct a knowledge graph of multiple RNAs and diseases. After that, we use a recursive method to build multi-hop subgraphs and optimize graph attention mechanism by gating mechanism, mining local depth information. At the same time, the model uses multi-head attention mechanism to balance global and local depth features of graphs, and generate CDA prediction scores. KGRACDA surpasses other methods by capturing local and global depth information related to CDA. We update an interactive web platform HNRBase v2.0, which visualizes circRNA data, and allows users to download data and predict CDA using model.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2133-2144"},"PeriodicalIF":3.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142017376","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

Parallel Convolutional Contrastive Learning Method for Enzyme Function Prediction 用于酶功能预测的并行卷积对比学习方法。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-21 DOI: 10.1109/TCBB.2024.3447037

Xindi Yu;Shusen Zhou;Mujun Zang;Qingjun Wang;Chanjuan Liu;Tong Liu

{"title":"Parallel Convolutional Contrastive Learning Method for Enzyme Function Prediction","authors":"Xindi Yu;Shusen Zhou;Mujun Zang;Qingjun Wang;Chanjuan Liu;Tong Liu","doi":"10.1109/TCBB.2024.3447037","DOIUrl":"10.1109/TCBB.2024.3447037","url":null,"abstract":"The function labeling of enzymes has a wide range of application value in the medical field, industrial biology and other fields. Scientists define enzyme categories by enzyme commission (EC) numbers. At present, although there are some tools for enzyme function prediction, their effects have not reached the application level. To improve the precision of enzyme function prediction, we propose a parallel convolutional contrastive learning (PCCL) method to predict enzyme functions. First, we use the advanced protein language model ESM-2 to preprocess the protein sequences. Second, PCCL combines convolutional neural networks (CNNs) and contrastive learning to improve the prediction precision of multifunctional enzymes. Contrastive learning can make the model better deal with the problem of class imbalance. Finally, the deep learning framework is mainly composed of three parallel CNNs for fully extracting sample features. we compare PCCL with state-of-art enzyme function prediction methods based on three evaluation metrics. The performance of our model improves on both two test sets. Especially on the smaller test set, PCCL improves the AUC by 2.57%.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2604-2609"},"PeriodicalIF":3.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142017377","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

Integrating K+ Entities Into Coreference Resolution on Biomedical Texts 将 K+ 实体整合到生物医学文本的核心参照解析中。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-21 DOI: 10.1109/TCBB.2024.3447273

Yufei Li;Xiaoyong Ma;Xiangyu Zhou;Penghzhen Cheng;Kai He;Tieliang Gong;Chen Li

{"title":"Integrating K+ Entities Into Coreference Resolution on Biomedical Texts","authors":"Yufei Li;Xiaoyong Ma;Xiangyu Zhou;Penghzhen Cheng;Kai He;Tieliang Gong;Chen Li","doi":"10.1109/TCBB.2024.3447273","DOIUrl":"10.1109/TCBB.2024.3447273","url":null,"abstract":"Biomedical Coreference Resolution focuses on identifying the coreferences in biomedical texts, which normally consists of two parts: (i) mention detection to identify textual representation of biological entities and (ii) finding their coreference links. Recently, a popular approach to enhance the task is to embed knowledge base into deep neural networks. However, the way in which these methods integrate knowledge leads to the shortcoming that such knowledge may play a larger role in mention detection than coreference resolution. Specifically, they tend to integrate knowledge prior to mention detection, as part of the embeddings. Besides, they primarily focus on mention-dependent knowledge (KBase), i.e., knowledge entities directly related to mentions, while ignores the correlated knowledge (K+) between mentions in the mention-pair. For mentions with significant differences in word form, this may limit their ability to extract potential correlations between those mentions. Thus, this paper develops a novel model to integrate both KBase and K+ entities and achieves the state-of-the-art performance on BioNLP and CRAFT-CR datasets. Empirical studies on mention detection with different length reveals the effectiveness of the KBase entities. The evaluation on cross-sentence and match/mismatch coreference further demonstrate the superiority of the K+ entities in extracting background potential correlation between mentions.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2145-2155"},"PeriodicalIF":3.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142017375","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

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