TarKG: A Comprehensive Biomedical Knowledge Graph for Target Discovery DOI Creative Commons
Cong Zhou, Chuipu Cai, Xiaotian Huang

и другие.

Bioinformatics, Год журнала: 2024, Номер 40(10)

Опубликована: Окт. 1, 2024

Abstract Motivation Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such repositioning drug–target interactions, leaving gap construction tailored for target discovery. Results We established comprehensive KG focusing discovery, termed TarKG, by integrating seven existing KGs, nine public databases, traditional Chinese medicine knowledge databases. TarKG consists 1 143 313 entities 32 806 467 relations across 15 entity categories 171 relation types, all centered around 3 core types: Disease, Gene, Compound. provides specialized knowledges including chemical structures, protein sequences, or text descriptions. By using different embedding algorithms, we assessed completion capabilities particularly disease–target link prediction. In case studies, further examined TarKG’s ability to predict potential targets Alzheimer’s disease (AD) identify diseases potentially associated with metallo-deubiquitinase CSN5, literature analysis validation. Furthermore, provided user-friendly web server (https://tarkg.ddtmlab.org) that enables users perform retrieval inference TarKG. Availability implementation accessible at https://tarkg.ddtmlab.org.

Язык: Английский

Neighborhood Topology-Aware Knowledge Graph Learning and Microbial Preference Inferring for Drug-Microbe Association Prediction DOI
Jing Gu, Tiangang Zhang,

Yihang Gao

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер 65(1), С. 435 - 445

Опубликована: Янв. 2, 2025

The human microbiota may influence the effectiveness of drug therapy by activating or inactivating pharmacological properties drugs. Computational methods have demonstrated their ability to screen reliable microbe-drug associations and uncover mechanism which drugs exert functions. However, previous prediction failed completely exploit neighborhood topologies microbe entities diverse correlations between entity pair other entities. In addition, they ignored case that a prefers associate with its own specific A novel method, PCMDA, was proposed learning entities, inferring association preferences, integrating features each based on multiple biological premises. First, knowledge graph consisting microbe, disease, is established help subsequent integration topological structure similarity, interaction, relationship any two We generate various embeddings for (or drug) through random walks restarts microbe-disease-drug graph. Distance-level attention designed adaptively fuse covering ranges. Second, imply latent relationships while relational are derived from semantics connections among fused module multilayer perceptron networks. Third, considering preference tends especially group drugs, information-level integrate dependency microbial candidate drug. Finally, dual-gated network encode perspectives. comparative experiments seven state-of-the-art demonstrate PCMDA's superior performance prediction. studies three recall rate evaluation top-ranked candidates indicate PCMDA has capability discovering microbes associated datasets source codes freely available at https://github.com/pingxuan-hlju/pcmda.

Язык: Английский

Процитировано

1
KNDM: A Knowledge Graph Transformer and Node Category Sensitive Contrastive Learning Model for Drug and Microbe Association Prediction DOI
Dongliang Chen, Tiangang Zhang, Hui Cui

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Апрель 23, 2025

It has been proven that the microbiome in human bodies can promote or inhibit treatment effects of drugs by affecting their toxicities and activities. Therefore, identifying drug-related microbes helps understanding how exert functions under influence these microbes. Most recent methods for microbe prediction are developed based on graph learning. However, those fail to fully utilize diverse characteristics drug entities from perspective a knowledge graph, as well contextual relationships among multiple meta-paths meta-path perspective. Moreover, previous overlook consistency between entity features derived node semantic extracted meta-paths. To address limitations, we propose knowledge-graph transformer category-sensitive contrastive learning-based association model (KNDM). This learns entities, encodes across meta-paths, integrates feature consistency. First, construct consisting which aids revealing similarities associations any two entities. Second, considering heterogeneity an integrate diversity types various them. Third, constructed capture embed nodes. A learning strategy with recursive gating is proposed specific individual while fusing Finally, develop node-category-sensitive enhance features. Extensive experiments demonstrate KNDM outperforms eight state-of-the-art drug-microbe models, ablation studies validate effectiveness its key innovations. Additionally, case candidate associated three drugs-curcumin, epigallocatechin gallate, ciprofloxacin-further showcase KNDM's capability identify potential associations.

Язык: Английский

Процитировано

0
Boosting Drug-Disease Association Prediction for Drug Repositioning via Dual-Feature Extraction and Cross-Dual-Domain Decoding DOI
Enqiang Zhu, Xiang Li, Chanjuan Liu

и другие.

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Апрель 25, 2025

The extraction of biomedical data has significant academic and practical value in contemporary sciences. In recent years, drug repositioning, a cost-effective strategy for development by discovering new indications approved drugs, gained increasing attention. However, many existing repositioning methods focus on mining information from adjacent nodes networks without considering the potential inter-relationships between feature spaces drugs diseases. This can lead to inaccurate encoding, resulting biased mined drug-disease association information. To address this limitation, we propose model called Dual-Feature Drug Repurposing Neural Network (DFDRNN). DFDRNN allows two features (similarity association) encode A self-attention mechanism is utilized extract neighbor It incorporates dual-feature modules: single-domain (SDDFE) module extracting within single domain (drugs or diseases) cross-domain (CDDFE) across domains. By utilizing these modules, ensure more appropriate encoding cross-dual-domain decoder also designed predict associations both Our proposed outperforms six state-of-the-art four benchmark sets, achieving an average AUROC 0.946 AUPR 0.597. Case studies three diseases show that be applied real-world scenarios, demonstrating its repositioning.

Язык: Английский

Процитировано

0
Adaptive debiasing learning for drug repositioning DOI
Yajie Meng, Yi Wang, Xinrong Hu

и другие.

Journal of Biomedical Informatics, Год журнала: 2025, Номер 167, С. 104843 - 104843

Опубликована: Май 17, 2025

Язык: Английский

Процитировано

0
Drug repositioning by collaborative learning based on graph convolutional inductive network DOI Creative Commons
Zhixia Teng,

Yongliang Li,

Zhen Tian

и другие.

Future Generation Computer Systems, Год журнала: 2024, Номер 162, С. 107491 - 107491

Опубликована: Авг. 22, 2024

Язык: Английский

Процитировано

0
TarKG: A Comprehensive Biomedical Knowledge Graph for Target Discovery DOI Creative Commons
Cong Zhou, Chuipu Cai, Xiaotian Huang

и другие.

Bioinformatics, Год журнала: 2024, Номер 40(10)

Опубликована: Окт. 1, 2024

Abstract Motivation Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such repositioning drug–target interactions, leaving gap construction tailored for target discovery. Results We established comprehensive KG focusing discovery, termed TarKG, by integrating seven existing KGs, nine public databases, traditional Chinese medicine knowledge databases. TarKG consists 1 143 313 entities 32 806 467 relations across 15 entity categories 171 relation types, all centered around 3 core types: Disease, Gene, Compound. provides specialized knowledges including chemical structures, protein sequences, or text descriptions. By using different embedding algorithms, we assessed completion capabilities particularly disease–target link prediction. In case studies, further examined TarKG’s ability to predict potential targets Alzheimer’s disease (AD) identify diseases potentially associated with metallo-deubiquitinase CSN5, literature analysis validation. Furthermore, provided user-friendly web server (https://tarkg.ddtmlab.org) that enables users perform retrieval inference TarKG. Availability implementation accessible at https://tarkg.ddtmlab.org.

Язык: Английский

Процитировано

0