IEEE/ACM Transactions on Computational Biology and Bioinformatics最新文献_第6页

APMG: 3D Molecule Generation Driven by Atomic Chemical Properties APMG：由原子化学性质驱动的三维分子生成

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-09-10 DOI: 10.1109/TCBB.2024.3457807

Yang Hua;Zhenhua Feng;Xiaoning Song;Hui Li;Tianyang Xu;Xiao-Jun Wu;Dong-Jun Yu

{"title":"APMG: 3D Molecule Generation Driven by Atomic Chemical Properties","authors":"Yang Hua;Zhenhua Feng;Xiaoning Song;Hui Li;Tianyang Xu;Xiao-Jun Wu;Dong-Jun Yu","doi":"10.1109/TCBB.2024.3457807","DOIUrl":"10.1109/TCBB.2024.3457807","url":null,"abstract":"Recently, mask-fill-based 3D Molecular Generation (MG) methods have become very popular in virtual drug design. However, the existing MG methods ignore the chemical properties of atoms and contain inappropriate atomic position training data, which limits their generation capability. To mitigate the above issues, this paper presents a novel mask-fill-based 3D molecule generation model driven by atomic chemical properties (APMG). Specifically, we construct a new attention-MPNN-based encoder and introduce the electronic information into atom representations to enrich chemical properties. Also, a multi-functional classifier is designed to predict the electronic information of each generated atom, guiding the type prediction of elements and bonds. By design, the proposed method uses the chemical properties of atoms and their correlations for high-quality molecule generation. Second, to optimize the atomic position training data, we propose a novel atomic training position generation approach using the Chi-Square distribution. We evaluate our APMG method on the CrossDocked dataset and visualize the docking states of the pockets and generated molecules. The obtained results demonstrate the superiority and merits of APMG over the state-of-the-art approaches.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2269-2279"},"PeriodicalIF":3.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182853","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

Combining Zhegalkin Polynomials and SAT Solving for Context-Specific Boolean Modeling of Biological Systems 结合哲加金多项式和 SAT 求解，建立生物系统的特定语境布尔模型

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-09-10 DOI: 10.1109/TCBB.2024.3456302

Vincent Deman;Marine Ciantar;Laurent Naudin;Philippe Castera;Anne-Sophie Beignon

{"title":"Combining Zhegalkin Polynomials and SAT Solving for Context-Specific Boolean Modeling of Biological Systems","authors":"Vincent Deman;Marine Ciantar;Laurent Naudin;Philippe Castera;Anne-Sophie Beignon","doi":"10.1109/TCBB.2024.3456302","DOIUrl":"10.1109/TCBB.2024.3456302","url":null,"abstract":"Large amounts of knowledge regarding biological processes are readily available in the literature and aggregated in diverse databases. Boolean networks are powerful tools to render that knowledge into models that can mimic and simulate biological phenomena at multiple scales. Yet, when a model is required to understand or predict the behavior of a biological system in given conditions, existing information often does not completely match this context. Networks built from only prior knowledge can overlook mechanisms, lack specificity, and just partially recapitulate experimental observations. To address this limitation, context-specific data needs to be integrated. However, the brute-force identification of qualitative rules matching these data becomes infeasible as the number of candidates explodes for increasingly complex systems. Here, we used Zhegalkin polynomials to transform this identification into a binary value assignment for exponentially fewer variables, which we addressed with a state-of-the-art SAT solver. We evaluated our implemented method alongside two widely recognized tools, CellNetOptimizer and Caspo-ts, on both artificial toy models and large-scale models based on experimental data from the HPN-DREAM challenge. Our approach demonstrated benchmark-leading capabilities on networks of significant size and intricate complexity. It thus appears promising for the \u0000<italic>in silico</i>\u0000 modeling of ever more comprehensive biological systems.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2188-2199"},"PeriodicalIF":3.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10671585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182852","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

An Automated Convergence Diagnostic for Phylogenetic MCMC Analyses 系统发育 MCMC 分析的自动收敛诊断方法

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-09-10 DOI: 10.1109/TCBB.2024.3457875

Lars Berling;Remco Bouckaert;Alex Gavryushkin

{"title":"An Automated Convergence Diagnostic for Phylogenetic MCMC Analyses","authors":"Lars Berling;Remco Bouckaert;Alex Gavryushkin","doi":"10.1109/TCBB.2024.3457875","DOIUrl":"10.1109/TCBB.2024.3457875","url":null,"abstract":"Assessing convergence of Markov chain Monte Carlo (MCMC) based analyses is crucial but challenging, especially so in high dimensional and complex spaces such as the space of phylogenetic trees (treespace). In practice, it is assumed that the target distribution is the unique stationary distribution of the MCMC and convergence is achieved when samples appear to be stationary. Here we leverage recent advances in computational geometry of the treespace and introduce a method that combines classical statistical techniques and algorithms with geometric properties of the treespace to automatically evaluate and assess practical convergence of phylogenetic MCMC analyses. Our method monitors convergence across multiple MCMC chains and achieves high accuracy in detecting both practical convergence and convergence issues within treespace. Furthermore, our approach is developed to allow for real-time evaluation during the MCMC algorithm run, eliminating any of the chain post-processing steps that are currently required. Our tool therefore improves reliability and efficiency of MCMC based phylogenetic inference methods and makes analyses easier to reproduce and compare. We demonstrate the efficacy of our diagnostic via a well-calibrated simulation study and provide examples of its performance on real data sets. Although our method performs well in practice, a significant part of the underlying treespace probability theory is still missing, which creates an excellent opportunity for future mathematical research in this area.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2246-2257"},"PeriodicalIF":3.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182854","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

Bridging Between Deviation Indices for Non-Tree-Based Phylogenetic Networks 非基于树的系统发育网络偏差指数之间的衔接

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-09-09 DOI: 10.1109/TCBB.2024.3456575

Takatora Suzuki;Han Guo;Momoko Hayamizu

{"title":"Bridging Between Deviation Indices for Non-Tree-Based Phylogenetic Networks","authors":"Takatora Suzuki;Han Guo;Momoko Hayamizu","doi":"10.1109/TCBB.2024.3456575","DOIUrl":"10.1109/TCBB.2024.3456575","url":null,"abstract":"Phylogenetic networks are a useful model that can represent reticulate evolution and complex biological data. In recent years, mathematical and computational aspects of tree-based networks have been well studied. However, not all phylogenetic networks are tree-based, so it is meaningful to consider how close a given network is to being tree-based; Francis–Steel–Semple (2018) proposed several different indices to measure the degree of deviation of a phylogenetic network from being tree-based. One is the minimum number of leaves that need to be added to convert a given network to tree-based, and another is the number of vertices that are not included in the largest subtree covering its leaf-set. Both values are zero if and only if the network is tree-based. Both deviation indices can be computed efficiently, but the relationship between the above two is unknown, as each has been studied using different approaches. In this study, we derive a tight inequality for the values of the two measures and also give a characterisation of phylogenetic networks such that they coincide. This characterisation yields a new efficient algorithm for the Maximum Covering Subtree Problem based on the maximal zig-zag trail decomposition.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2226-2234"},"PeriodicalIF":3.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182857","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

Relation Extraction in Biomedical Texts: A Cross-Sentence Approach 生物医学文本中的关系提取：跨句子方法

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-09-06 DOI: 10.1109/TCBB.2024.3451348

Zhijing Li;Liwei Tian;Yiping Jiang;Yucheng Huang

{"title":"Relation Extraction in Biomedical Texts: A Cross-Sentence Approach","authors":"Zhijing Li;Liwei Tian;Yiping Jiang;Yucheng Huang","doi":"10.1109/TCBB.2024.3451348","DOIUrl":"10.1109/TCBB.2024.3451348","url":null,"abstract":"Relation extraction, a crucial task in understanding the intricate relationships between entities in biomedical domains, has predominantly focused on binary relations within single sentences. However, in practical biomedical scenarios, relationships often extend across multiple sentences, leading to extraction errors with potential impacts on clinical decision-making and medical diagnosis. To overcome this limitation, we present a novel cross-sentence relation extraction framework that integrates and enhances coreference resolution and relation extraction models. Coreference resolution serves as the foundation, breaking sentence boundaries and linking entities across sentences. Our framework incorporates pre-trained deep language representations and leverages graph LSTMs to effectively model cross-sentence entity mentions. The use of a self-attentive Transformer architecture and external semantic information further enhances the modeling of intricate relationships. Comprehensive experiments conducted on two standard datasets, namely the BioNLP dataset and THYME dataset, demonstrate the state-of-the-art performance of our proposed approach.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2156-2166"},"PeriodicalIF":3.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142977","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

CTsynther: Contrastive Transformer Model for End-to-End Retrosynthesis Prediction CTsynther：用于端到端逆合成预测的对比变换器模型。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-09-06 DOI: 10.1109/TCBB.2024.3455381

Hao Lu;Zhiqiang Wei;Kun Zhang;Xuze Wang;Liaqat Ali;Hao Liu

{"title":"CTsynther: Contrastive Transformer Model for End-to-End Retrosynthesis Prediction","authors":"Hao Lu;Zhiqiang Wei;Kun Zhang;Xuze Wang;Liaqat Ali;Hao Liu","doi":"10.1109/TCBB.2024.3455381","DOIUrl":"10.1109/TCBB.2024.3455381","url":null,"abstract":"Retrosynthesis prediction is a fundamental problem in organic chemistry and drug synthesis. We proposed an end-to-end deep learning model called CTsynther (Contrastive Transformer for single-step retrosynthesis prediction model) that could provide single-step retrosynthesis prediction without external reaction templates or specialized knowledge. The model introduced the concept of contrastive learning in Transformer architecture and employed a contrastive learning language representation model at the SMILES sentence level to enhance model inference by learning similarities and differences between various samples. Mixed global and local attention mechanisms allow the model to capture features and dependencies between different atoms to improve generalization. We further investigated the embedding representations of SMILES learned automatically from the model. Visualization results show that the model could effectively acquire information about identical molecules and improve prediction performance. Experiments showed that the accuracy of retrosynthesis reached 53.5% and 64.4% for with and without reaction types, respectively. The validity of the predicted reactants is improved, showing competitiveness compared with semi-template methods.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2235-2245"},"PeriodicalIF":3.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142976","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

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-09-03 DOI: 10.1109/TCBB.2024.3453499

Bin Liu;Grigorios Tsoumakas

{"title":"Integrating Similarities via Local Interaction Consistency and Optimizing Area Under the Curve Measures via Matrix Factorization for Drug-Target Interaction Prediction","authors":"Bin Liu;Grigorios Tsoumakas","doi":"10.1109/TCBB.2024.3453499","DOIUrl":"10.1109/TCBB.2024.3453499","url":null,"abstract":"In drug discovery, identifying drug-target interactions (DTIs) via experimental approaches is a tedious and expensive procedure. Computational methods efficiently predict DTIs and recommend a small part of potential interacting pairs for further experimental confirmation, accelerating the drug discovery process. Although fusing heterogeneous drug and target similarities can improve the prediction ability, the existing similarity combination methods ignore the interaction consistency for neighbour entities. Furthermore, area under the precision-recall curve (AUPR) and area under the receiver operating characteristic curve (AUC) are two widely used evaluation metrics in DTI prediction. However, the two metrics are seldom considered as losses within existing DTI prediction methods. We propose a local interaction consistency (LIC) aware similarity integration method to fuse vital information from diverse views for DTI prediction models. Furthermore, we propose two matrix factorization (MF) methods that optimize AUPR and AUC using convex surrogate losses respectively, and then develop an ensemble MF approach that takes advantage of the two area under the curve metrics by combining the two single metric based MF models. Experimental results under different prediction settings show that the proposed methods outperform various competitors in terms of the metric(s) they optimize and are reliable in discovering potential new DTIs.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2212-2225"},"PeriodicalIF":3.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142125626","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

LKLPDA: A Low-Rank Fast Kernel Learning Approach for Predicting piRNA-Disease Associations LKLPDA：用于预测 piRNA 与疾病关联的低链快速核学习方法

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-30 DOI: 10.1109/TCBB.2024.3452055

Qingzhou Shi;Kai Zheng;Haoyuan Li;Bo Wang;Xiao Liang;Xinyu Li;Jianxin Wang

{"title":"LKLPDA: A Low-Rank Fast Kernel Learning Approach for Predicting piRNA-Disease Associations","authors":"Qingzhou Shi;Kai Zheng;Haoyuan Li;Bo Wang;Xiao Liang;Xinyu Li;Jianxin Wang","doi":"10.1109/TCBB.2024.3452055","DOIUrl":"10.1109/TCBB.2024.3452055","url":null,"abstract":"Piwi-interacting RNAs (piRNAs) are increasingly recognized as potential biomarkers for various diseases. Investig-ating the complex relationship between piRNAs and diseases through computational methods can reduce the costs and risks associated with biological experiments. Fast kernel learning (FKL) is a classical method for multi-source data fusion that is widely employed in association prediction research. However, biological networks are noisy due to the limitations of measurement technology and inherent natural variation, which can hamper the effectiveness of the network-based ideal kernel. The conventional FKL method does not address this issue. In this study, we propose a low-rank fast kernel learning (LRFKL) algorithm, which consists of low-rank representation (LRR) and the FKL algorithm. The LRFKL algorithm is designed to mitigate the effects of noise on the network-based ideal kernel. Using LRFKL, we propose a novel approach for predicting piRNA-disease associations called LKLPDA. Specifically, we first compute the similarity matrices for piRNAs and diseases. Then we use the LRFKL to fuse the similarity matrices for piRNAs and diseases separately. Finally, the LKLPDA employs AutoGluon-Tabular for predictive analysis. Computational results show that LKLPDA effectively predicts piRNA-disease associations with higher accuracy compared to previous methods. In addition, case studies confirm the reliability of the model in predicting piRNA-disease associations.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2179-2187"},"PeriodicalIF":3.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142106990","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

MMD-DTA: A Multi-Modal Deep Learning Framework for Drug-Target Binding Affinity and Binding Region Prediction MMD-DTA：用于药物与目标结合亲和力和结合区域预测的多模态深度学习框架。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-29 DOI: 10.1109/TCBB.2024.3451985

Qi Zhang;Yuxiao Wei;Bo Liao;Liwei Liu;Shengli Zhang

{"title":"MMD-DTA: A Multi-Modal Deep Learning Framework for Drug-Target Binding Affinity and Binding Region Prediction","authors":"Qi Zhang;Yuxiao Wei;Bo Liao;Liwei Liu;Shengli Zhang","doi":"10.1109/TCBB.2024.3451985","DOIUrl":"10.1109/TCBB.2024.3451985","url":null,"abstract":"The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this study, we propose a multi-modal deep learning framework called MMD-DTA for predicting drug-target binding affinity and binding regions. The model can predict DTA while simultaneously learning the binding regions of drug-target interactions through unsupervised learning. To achieve this, MMD-DTA first uses graph neural networks and target structural feature extraction network to extract multi-modal information from the sequences and structures of drugs and targets. It then utilizes the feature interaction and fusion modules to generate interaction descriptors for predicting DTA and interaction strength for binding region prediction. Our experimental results demonstrate that MMD-DTA outperforms existing models based on key evaluation metrics. Furthermore, external validation results indicate that MMD-DTA enhances the generalization capability of the model by integrating sequence and structural information of drugs and targets. The model trained on the benchmark dataset can effectively generalize to independent virtual screening tasks. The visualization of drug-target binding region prediction showcases the interpretability of MMD-DTA, providing valuable insights into the functional regions of drug molecules that interact with proteins.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2200-2211"},"PeriodicalIF":3.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142106991","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

Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation 用于生物医学假设生成的多源时态知识图谱对比。

IF 3.6 3区生物学

IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-28 DOI: 10.1109/TCBB.2024.3451051

Huiwei Zhou;Wenchu Li;Weihong Yao;Yingyu Lin;Lei Du

{"title":"Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation","authors":"Huiwei Zhou;Wenchu Li;Weihong Yao;Yingyu Lin;Lei Du","doi":"10.1109/TCBB.2024.3451051","DOIUrl":"10.1109/TCBB.2024.3451051","url":null,"abstract":"Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses from existing scientific literature. Most existing studies focused on modeling static snapshots of the corpus, neglecting the temporal evolution of scientific terms. Despite recent efforts to learn term evolution from Knowledge Bases (KBs) for HG, the temporal information from multi-source KBs is still overlooked, which contains important, up-to-date knowledge. In this paper, an innovative Temporal Contrastive Learning (TCL) framework is introduced to uncover latent associations between entities by jointly modeling their co-evolution across multi-source temporal KBs. Specifically, we first construct a temporal relation graph based on PubMed papers and a biomedical relation database (such as Comparative Toxicogenomics Database (CTD)). Then the constructed temporal relation graph and a temporal concept graph (such as Medical Subject Headings (MeSH)) are used to train two GCN-based recurrent networks for learning the entity temporal evolutional embeddings, respectively. Finally, a cross-view temporal prediction task is designed for learning knowledge enriched temporal embeddings by contrasting the temporal embeddings learned from the two Temporal Knowledge Graphs (TKGs). Findings from experiments conducted on three real-world biomedical term relationship datasets demonstrate that the proposed approach is clearly superior to approaches based on single TKG, achieving the state-of-the-art performance.","PeriodicalId":13344,"journal":{"name":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","volume":"21 6","pages":"2102-2112"},"PeriodicalIF":3.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142086095","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