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Xiao Y, Zhang S, Zhou H, Li M, Yang H, Zhang R. FuseLinker: Leveraging LLM's pre-trained text embeddings and domain knowledge to enhance GNN-based link prediction on biomedical knowledge graphs. J Biomed Inform 2024; 158:104730. [PMID: 39326691 DOI: 10.1016/j.jbi.2024.104730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 09/20/2024] [Accepted: 09/23/2024] [Indexed: 09/28/2024]
Abstract
OBJECTIVE To develop the FuseLinker, a novel link prediction framework for biomedical knowledge graphs (BKGs), which fully exploits the graph's structural, textual and domain knowledge information. We evaluated the utility of FuseLinker in the graph-based drug repurposing task through detailed case studies. METHODS FuseLinker leverages fused pre-trained text embedding and domain knowledge embedding to enhance the graph neural network (GNN)-based link prediction model tailored for BKGs. This framework includes three parts: a) obtain text embeddings for BKGs using embedding-visible large language models (LLMs), b) learn the representations of medical ontology as domain knowledge information by employing the Poincaré graph embedding method, and c) fuse these embeddings and further learn the graph structure representations of BKGs by applying a GNN-based link prediction model. We evaluated FuseLinker against traditional knowledge graph embedding models and a conventional GNN-based link prediction model across four public BKG datasets. Additionally, we examined the impact of using different embedding-visible LLMs on FuseLinker's performance. Finally, we investigated FuseLinker's ability to generate medical hypotheses through two drug repurposing case studies for Sorafenib and Parkinson's disease. RESULTS By comparing FuseLinker with baseline models on four BKGs, our method demonstrates superior performance. The Mean Reciprocal Rank (MRR) and Area Under receiver operating characteristic Curve (AUROC) for KEGG50k, Hetionet, SuppKG and ADInt are 0.969 and 0.987, 0.548 and 0.903, 0.739 and 0.928, and 0.831 and 0.890, respectively. CONCLUSION Our study demonstrates that FuseLinker is an effective novel link prediction framework that integrates multiple graph information and shows significant potential for practical applications in biomedical and clinical tasks. Source code and data are available at https://github.com/YKXia0/FuseLinker.
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Affiliation(s)
- Yongkang Xiao
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Sinian Zhang
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Huixue Zhou
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Mingchen Li
- Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Han Yang
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Rui Zhang
- Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
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Ni S, Kong X, Zhang Y, Chen Z, Wang Z, Fu Z, Huo R, Tong X, Qu N, Wu X, Wang K, Zhang W, Zhang R, Zhang Z, Shi J, Wang Y, Yang R, Li X, Zhang S, Zheng M. Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics. CELL GENOMICS 2024:100655. [PMID: 39303708 DOI: 10.1016/j.xgen.2024.100655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 07/04/2024] [Accepted: 08/20/2024] [Indexed: 09/22/2024]
Abstract
The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.
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Affiliation(s)
- Shengkun Ni
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xiangtai Kong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Yingying Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; The First Affiliated Hospital of USTC (Anhui Provincial Hospital), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Zhengyang Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Zhaokun Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Zunyun Fu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Ruifeng Huo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China
| | - Xiaochu Tong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Ning Qu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xiaolong Wu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Kun Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; The First Affiliated Hospital of USTC (Anhui Provincial Hospital), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Wei Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Runze Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Zimei Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; The First Affiliated Hospital of USTC (Anhui Provincial Hospital), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China
| | - Jiangshan Shi
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Yitian Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Ruirui Yang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China.
| | - Sulin Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China.
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China; School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
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Liu C, Li Z, Li J, Qu Y, Chang Y, Han Q, Cao L, Lin S. Research on Traditional Chinese Medicine: Domain Knowledge Graph Completion and Quality Evaluation. JMIR Med Inform 2024; 12:e55090. [PMID: 39094109 PMCID: PMC11329848 DOI: 10.2196/55090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 03/13/2024] [Accepted: 05/14/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Knowledge graphs (KGs) can integrate domain knowledge into a traditional Chinese medicine (TCM) intelligent syndrome differentiation model. However, the quality of current KGs in the TCM domain varies greatly, related to the lack of knowledge graph completion (KGC) and evaluation methods. OBJECTIVE This study aims to investigate KGC and evaluation methods tailored for TCM domain knowledge. METHODS In the KGC phase, according to the characteristics of TCM domain knowledge, we proposed a 3-step "entity-ontology-path" completion approach. This approach uses path reasoning, ontology rule reasoning, and association rules. In the KGC quality evaluation phase, we proposed a 3-dimensional evaluation framework that encompasses completeness, accuracy, and usability, using quantitative metrics such as complex network analysis, ontology reasoning, and graph representation. Furthermore, we compared the impact of different graph representation models on KG usability. RESULTS In the KGC phase, 52, 107, 27, and 479 triples were added by outlier analysis, rule-based reasoning, association rules, and path-based reasoning, respectively. In addition, rule-based reasoning identified 14 contradictory triples. In the KGC quality evaluation phase, in terms of completeness, KG had higher density and lower sparsity after completion, and there were no contradictory rules within the KG. In terms of accuracy, KG after completion was more consistent with prior knowledge. In terms of usability, the mean reciprocal ranking, mean rank, and hit rate of the first N tail entities predicted by the model (Hits@N) of the TransE, RotatE, DistMult, and ComplEx graph representation models all showed improvement after KGC. Among them, the RotatE model achieved the best representation. CONCLUSIONS The 3-step completion approach can effectively improve the completeness, accuracy, and availability of KGs, and the 3-dimensional evaluation framework can be used for comprehensive KGC evaluation. In the TCM field, the RotatE model performed better at KG representation.
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Affiliation(s)
- Chang Liu
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
- Breast Disease Specialist Hospital of Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
- Zhejiang Chinese Medical University and Gancao Doctor Chinese Medicine Artificial Intelligence Joint Engineering Center, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhan Li
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianmin Li
- Zhejiang Chinese Medical University and Gancao Doctor Chinese Medicine Artificial Intelligence Joint Engineering Center, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yiqian Qu
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
- Zhejiang Chinese Medical University and Gancao Doctor Chinese Medicine Artificial Intelligence Joint Engineering Center, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ying Chang
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Qing Han
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Lingyong Cao
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
- Zhejiang Chinese Medical University and Gancao Doctor Chinese Medicine Artificial Intelligence Joint Engineering Center, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuyuan Lin
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
- Zhejiang Chinese Medical University and Gancao Doctor Chinese Medicine Artificial Intelligence Joint Engineering Center, Zhejiang Chinese Medical University, Hangzhou, China
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Zheng S, Rao J, Zhang J, Zhou L, Xie J, Cohen E, Lu W, Li C, Yang Y. Cross-Modal Graph Contrastive Learning with Cellular Images. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2404845. [PMID: 39031820 PMCID: PMC11348220 DOI: 10.1002/advs.202404845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 06/03/2024] [Indexed: 07/22/2024]
Abstract
Constructing discriminative representations of molecules lies at the core of a number of domains such as drug discovery, chemistry, and medicine. State-of-the-art methods employ graph neural networks and self-supervised learning (SSL) to learn unlabeled data for structural representations, which can then be fine-tuned for downstream tasks. Albeit powerful, these methods are pre-trained solely on molecular structures and thus often struggle with tasks involved in intricate biological processes. Here, it is proposed to assist the learning of molecular representation by using the perturbed high-content cell microscopy images at the phenotypic level. To incorporate the cross-modal pre-training, a unified framework is constructed to align them through multiple types of contrastive loss functions, which is proven effective in the formulated novel tasks to retrieve the molecules and corresponding images mutually. More importantly, the model can infer functional molecules according to cellular images generated by genetic perturbations. In parallel, the proposed model can transfer non-trivially to molecular property predictions, and has shown great improvement over clinical outcome predictions. These results suggest that such cross-modality learning can bridge molecules and phenotype to play important roles in drug discovery.
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Affiliation(s)
- Shuangjia Zheng
- Global Institute of Future TechnologyShanghai Jiaotong University UniversityShanghai200240China
| | - Jiahua Rao
- School of Computer Science and EngineeringSun Yat‐sen UniversityGuangzhou510000China
| | | | - Lianyu Zhou
- School of InformaticsXiamen UniversityXiamen361005China
| | - Jiancong Xie
- School of Computer Science and EngineeringSun Yat‐sen UniversityGuangzhou510000China
| | - Ethan Cohen
- IBENS, Ecole Normale SupérieurePSL Research InstituteParisFrance
| | - Wei Lu
- Galixir TechnologiesShanghai200100China
| | | | - Yuedong Yang
- School of Computer Science and EngineeringSun Yat‐sen UniversityGuangzhou510000China
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5
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Devarakonda MV, Mohanty S, Sunkishala RR, Mallampalli N, Liu X. Clinical trial recommendations using Semantics-Based inductive inference and knowledge graph embeddings. J Biomed Inform 2024; 154:104627. [PMID: 38561170 DOI: 10.1016/j.jbi.2024.104627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 02/06/2024] [Accepted: 03/20/2024] [Indexed: 04/04/2024]
Abstract
OBJECTIVE Designing a new clinical trial entails many decisions, such as defining a cohort and setting the study objectives to name a few, and therefore can benefit from recommendations based on exhaustive mining of past clinical trial records. This study proposes an approach based on knowledge graph embeddings and semantics-driven inductive inference for generating such recommendations. METHOD The proposed recommendation methodology is based on neural embeddings trained on first-of-its-kind knowledge graph constructed from clinical trials data. The methodology includes design of a knowledge graph for clinical trial data, evaluation of various knowledge graph embedding techniques for it, application of a novel inductive inference method using these embeddings, and generation of recommendations for clinical trial design. The study uses freely available data from clinicaltrials.gov and related sources. RESULTS The proposed approach for recommendations obtained relevance scores ranging from 70% to 83%. These scores were determined by evaluating the text similarity of recommended elements to actual elements used in clinical trials that are in progress. Furthermore, the most pertinent recommendations were consistently located towards the top of the list, indicating the effectiveness of our method. CONCLUSION Our study suggests that inductive inference using node semantics is a viable approach for generating recommendations using graphs neural embeddings, and that there is a potential for improvement in training graph embeddings using node semantics.
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Affiliation(s)
| | | | | | | | - Xiong Liu
- Biomedical Research, Novartis, Cambridge, MA, USA
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6
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Rao J, Xie J, Yuan Q, Liu D, Wang Z, Lu Y, Zheng S, Yang Y. A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions. Nat Commun 2024; 15:4476. [PMID: 38796523 DOI: 10.1038/s41467-024-48801-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/14/2024] [Indexed: 05/28/2024] Open
Abstract
Protein functions are characterized by interactions with proteins, drugs, and other biomolecules. Understanding these interactions is essential for deciphering the molecular mechanisms underlying biological processes and developing new therapeutic strategies. Current computational methods mostly predict interactions based on either molecular network or structural information, without integrating them within a unified multi-scale framework. While a few multi-view learning methods are devoted to fusing the multi-scale information, these methods tend to rely intensively on a single scale and under-fitting the others, likely attributed to the imbalanced nature and inherent greediness of multi-scale learning. To alleviate the optimization imbalance, we present MUSE, a multi-scale representation learning framework based on a variant expectation maximization to optimize different scales in an alternating procedure over multiple iterations. This strategy efficiently fuses multi-scale information between atomic structure and molecular network scale through mutual supervision and iterative optimization. MUSE outperforms the current state-of-the-art models not only in molecular interaction (protein-protein, drug-protein, and drug-drug) tasks but also in protein interface prediction at the atomic structure scale. More importantly, the multi-scale learning framework shows potential for extension to other scales of computational drug discovery.
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Affiliation(s)
- Jiahua Rao
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Jiancong Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Qianmu Yuan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Deqin Liu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Zhen Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yutong Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
| | - Shuangjia Zheng
- Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China.
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
- Key Laboratory of Machine Intelligence and Advanced Computing (MOE), Sun Yat-sen University, Guangzhou, China.
- State Key Laboratory of Oncology in South China, Sun Yat-sen University, Guangzhou, China.
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Zhang Y, Deng Z, Xu X, Feng Y, Junliang S. Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review. J Chem Inf Model 2024; 64:2158-2173. [PMID: 37458400 DOI: 10.1021/acs.jcim.3c00582] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.
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Affiliation(s)
- Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Zengqian Deng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Xiaoyu Xu
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Yinfei Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao,266000,China
| | - Shang Junliang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276800, China
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Feng X, Ma Z, Yu C, Xin R. MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing. J Chem Inf Model 2024; 64:2654-2669. [PMID: 38373300 DOI: 10.1021/acs.jcim.3c01726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
As is well-known, the process of developing new drugs is extremely expensive, whereas drug repurposing represents a promising approach to augment the efficiency of new drug development. While this method can indeed spare us from expensive drug toxicity and safety experiments, it still demands a substantial amount of time to carry out precise efficacy experiments for specific diseases, thereby consuming a significant quantity of resources. Therefore, if we can prescreen potential other indications for selected drugs, it could result in substantial cost savings. In light of this, this paper introduces a drug repurposing recommendation model called MRNDR, which stands for Multi-head attention-based Recommendation Network for Drug Repurposing. This model serves as a prediction tool for drug-disease relationships, leveraging the multihead self-attention mechanism that demonstrates robust generalization capabilities. These capabilities stem not only from our extensive million-level training data set, BioRE (Biology Recommended Entity data), but also from the utilization of the WRDS (Weighted Representation Distance Score) algorithm proposed by us. The MRNDR model has achieved new state-of-the-art results on the GP-KG public data set, with an MRR (Mean Reciprocal Rank) score of 0.308 and a Hits@10 score of 0.628. This represents significant improvements of 4.7% (MRR) and 18.1% (Hits@10) over the current best-performing models. Additionally, to further validate the practical utility of the model, we examined results recommended by MRNDR that were not present in the training data set. Some of these recommendations have undergone clinical trials, as evidenced by their presence on ClinicalTrials.gov and the China Clinical Trials Center, indirectly confirming the applicability of MRNDR. The MRNDR model can predict the reusability of candidate drugs, reducing the need for manual expert assessments and enabling efficient drug repurposing.
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Affiliation(s)
- Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130012, P.R. China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, P.R. China
| | - Zhansen Ma
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
| | - Cuinan Yu
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
| | - Ruihao Xin
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
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9
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Wang Y, Yang Z, Yao Q. Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning. COMMUNICATIONS MEDICINE 2024; 4:59. [PMID: 38548835 PMCID: PMC10978847 DOI: 10.1038/s43856-024-00486-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 03/18/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND Discovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. However, they generally require a huge number of samples, while known DDIs are rare. METHODS In this work, we present KnowDDI, a graph neural network-based method that addresses the above challenge. KnowDDI enhances drug representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. Then, it learns a knowledge subgraph for each drug-pair to interpret the predicted DDI, where each of the edges is associated with a connection strength indicating the importance of a known DDI or resembling strength between a drug-pair whose connection is unknown. Thus, the lack of DDIs is implicitly compensated by the enriched drug representations and propagated drug similarities. RESULTS Here we show the evaluation results of KnowDDI on two benchmark DDI datasets. Results show that KnowDDI obtains the state-of-the-art prediction performance with better interpretability. We also find that KnowDDI suffers less than existing works given a sparser knowledge graph. This indicates that the propagated drug similarities play a more important role in compensating for the lack of DDIs when the drug representations are less enriched. CONCLUSIONS KnowDDI nicely combines the efficiency of deep learning techniques and the rich prior knowledge in biomedical knowledge graphs. As an original open-source tool, KnowDDI can help detect possible interactions in a broad range of relevant interaction prediction tasks, such as protein-protein interactions, drug-target interactions and disease-gene interactions, eventually promoting the development of biomedicine and healthcare.
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Affiliation(s)
| | - Zaifei Yang
- Baidu Research, Baidu Inc., Beijing, China
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Quanming Yao
- Department of Electronic Engineering, Tsinghua University, Beijing, China.
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Xie J, Rao J, Xie J, Zhao H, Yang Y. Predicting disease-gene associations through self-supervised mutual infomax graph convolution network. Comput Biol Med 2024; 170:108048. [PMID: 38310804 DOI: 10.1016/j.compbiomed.2024.108048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/19/2023] [Accepted: 01/26/2024] [Indexed: 02/06/2024]
Abstract
Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contribute to treatments, but a large number of associations remained unexplored. To identify novel disease-gene associations, many computational methods have been developed using disease and gene-related prior knowledge. However, these methods remain of relatively inferior performance due to the limited external data sources and the inevitable noise among the prior knowledge. In this study, we have developed a new method, Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN), to predict disease-gene associations under the guidance of external disease-disease and gene-gene collaborative graphs. The noises within the collaborative graphs were eliminated by maximizing the mutual information between nodes and neighbors through a graphical mutual infomax layer. In parallel, the node interactions were strengthened by a novel informative message passing layer to improve the learning ability of graph neural network. The extensive experiments showed that our model achieved performance improvement over the state-of-art method by more than 8 % on AUC. The datasets, source codes and trained models of MiGCN are available at https://github.com/biomed-AI/MiGCN.
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Affiliation(s)
- Jiancong Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510000, China
| | - Jiahua Rao
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510000, China
| | - Junjie Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510000, China
| | - Huiying Zhao
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510000, China.
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510000, China.
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11
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Yu G, Ye Q, Ruan T. Enhancing Error Detection on Medical Knowledge Graphs via Intrinsic Label. Bioengineering (Basel) 2024; 11:225. [PMID: 38534499 DOI: 10.3390/bioengineering11030225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/21/2024] [Accepted: 02/24/2024] [Indexed: 03/28/2024] Open
Abstract
The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995.
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Affiliation(s)
- Guangya Yu
- Zhejiang Laboratory, Hangzhou 311121, China
- School of Information Science and Technology, East China University of Science and Technology, Shanghai 200237, China
| | - Qi Ye
- School of Information Science and Technology, East China University of Science and Technology, Shanghai 200237, China
| | - Tong Ruan
- School of Information Science and Technology, East China University of Science and Technology, Shanghai 200237, China
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12
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Luo Y, Liu XY, Yang K, Huang K, Hong M, Zhang J, Wu Y, Nie Z. Toward Unified AI Drug Discovery with Multimodal Knowledge. HEALTH DATA SCIENCE 2024; 4:0113. [PMID: 38486623 PMCID: PMC10886071 DOI: 10.34133/hds.0113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 01/25/2024] [Indexed: 03/17/2024]
Abstract
Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical literature. Existing multimodal approaches in AI drug discovery integrate either structured or unstructured knowledge independently, which compromises the holistic understanding of biomolecules. Besides, they fail to address the missing modality problem, where multimodal information is missing for novel drugs and proteins. Methods: In this work, we present KEDD, a unified, end-to-end deep learning framework that jointly incorporates both structured and unstructured knowledge for vast AI drug discovery tasks. The framework first incorporates independent representation learning models to extract the underlying characteristics from each modality. Then, it applies a feature fusion technique to calculate the prediction results. To mitigate the missing modality problem, we leverage sparse attention and a modality masking technique to reconstruct the missing features based on top relevant molecules. Results: Benefiting from structured and unstructured knowledge, our framework achieves a deeper understanding of biomolecules. KEDD outperforms state-of-the-art models by an average of 5.2% on drug-target interaction prediction, 2.6% on drug property prediction, 1.2% on drug-drug interaction prediction, and 4.1% on protein-protein interaction prediction. Through qualitative analysis, we reveal KEDD's promising potential in assisting real-world applications. Conclusions: By incorporating biomolecular expertise from multimodal knowledge, KEDD bears promise in accelerating drug discovery.
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Affiliation(s)
- Yizhen Luo
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
- Department of Computer Science and Technology,
Tsinghua University, Beijing, China
| | - Xing Yi Liu
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
| | - Kai Yang
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
| | - Kui Huang
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
- School of Software and Microelectronics,
Peking University, Beijing, China
| | - Massimo Hong
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
- Department of Computer Science and Technology,
Tsinghua University, Beijing, China
| | - Jiahuan Zhang
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
| | - Yushuai Wu
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
| | - Zaiqing Nie
- Institute for AI Industry Research (AIR),
Tsinghua University, Beijing, China
- Beijing Academy of Artificial Intelligence (BAAI), Beijing, China
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13
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Han S, Lee JE, Kang S, So M, Jin H, Lee JH, Baek S, Jun H, Kim TY, Lee YS. Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis. Brief Bioinform 2024; 25:bbae035. [PMID: 38349059 PMCID: PMC10862655 DOI: 10.1093/bib/bbae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/28/2023] [Indexed: 02/15/2024] Open
Abstract
Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms-a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial-mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.
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Affiliation(s)
- Seokjin Han
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Ji Eun Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Seolhee Kang
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Minyoung So
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hee Jin
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
| | - Jang Ho Lee
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Sunghyeob Baek
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Hyungjin Jun
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Tae Yong Kim
- Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
| | - Yun-Sil Lee
- College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
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14
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Gao Z, Ding P, Xu R. IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder. Pharmacol Res 2024; 199:106960. [PMID: 37832859 DOI: 10.1016/j.phrs.2023.106960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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15
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Abstract
Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing or visualizing that data, and generating novel insights through the application of machine learning approaches. Knowledge graphs put data into context and therefore offer the opportunity to generate explainable predictions, which is a key topic in contemporary artificial intelligence. In this chapter, we outline some of the factors that need to be considered when constructing biomedical knowledge graphs, examine recent advances in mining such systems to gain insights for drug discovery, and identify potential future areas for further development.
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Affiliation(s)
- Tim James
- Evotec (UK) Ltd., Abingdon, Oxfordshire, UK.
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16
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Liu Y, Sang G, Liu Z, Pan Y, Cheng J, Zhang Y. MPTN: A message-passing transformer network for drug repurposing from knowledge graph. Comput Biol Med 2024; 168:107800. [PMID: 38043469 DOI: 10.1016/j.compbiomed.2023.107800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/09/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.
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Affiliation(s)
- Yuanxin Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Guoming Sang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Zhi Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yilin Pan
- School of Artificial Intelligence, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Junkai Cheng
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yijia Zhang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China.
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17
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Zhang Y, Sui X, Pan F, Yu K, Li K, Tian S, Erdengasileng A, Han Q, Wang W, Wang J, Wang J, Sun D, Chung H, Zhou J, Zhou E, Lee B, Zhang P, Qiu X, Zhao T, Zhang J. BioKG: a comprehensive, large-scale biomedical knowledge graph for AI-powered, data-driven biomedical research. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.13.562216. [PMID: 38168218 PMCID: PMC10760044 DOI: 10.1101/2023.10.13.562216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
To cope with the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have emerged as a powerful data structure for integrating large volumes of heterogeneous data to facilitate accurate and efficient information retrieval and automated knowledge discovery (AKD). However, transforming unstructured content from scientific literature into KGs has remained a significant challenge, with previous methods unable to achieve human-level accuracy. In this study, we utilized an information extraction pipeline that won first place in the LitCoin NLP Challenge to construct a largescale KG using all PubMed abstracts. The quality of the large-scale information extraction rivals that of human expert annotations, signaling a new era of automatic, high-quality database construction from literature. Our extracted information markedly surpasses the amount of content in manually curated public databases. To enhance the KG's comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. The comprehensive KG enabled rigorous performance evaluation of AKD, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and achieved unprecedented results for drug target identification and drug repurposing. Taking lung cancer as an example, we found that 40% of drug targets reported in literature could have been predicted by our algorithm about 15 years ago in a retrospective study, demonstrating that substantial acceleration in scientific discovery could be achieved through automated hypotheses generation and timely dissemination. A cloud-based platform (https://www.biokde.com) was developed for academic users to freely access this rich structured data and associated tools.
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Affiliation(s)
- Yuan Zhang
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | - Xin Sui
- Insilicom LLC, Tallahassee, FL 32303
| | - Feng Pan
- Insilicom LLC, Tallahassee, FL 32303
| | | | - Keqiao Li
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | - Shubo Tian
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | | | - Qing Han
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | - Wanjing Wang
- Department of Statistics, Florida State University, Tallahassee, FL 32306
| | | | - Jian Wang
- 977 Wisteria Ter., Sunnyvale, CA 94086
| | | | | | - Jun Zhou
- Insilicom LLC, Tallahassee, FL 32303
| | - Eric Zhou
- Insilicom LLC, Tallahassee, FL 32303
| | - Ben Lee
- Insilicom LLC, Tallahassee, FL 32303
| | - Peili Zhang
- Forward Informatics, Winchester, Massachusetts, 01890
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642
| | - Tingting Zhao
- Department of Geography, Florida State University, Tallahassee, FL 32306
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL 32306
- Insilicom LLC, Tallahassee, FL 32303
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18
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Chen J, Gu Z, Lai L, Pei J. In silico protein function prediction: the rise of machine learning-based approaches. MEDICAL REVIEW (2021) 2023; 3:487-510. [PMID: 38282798 PMCID: PMC10808870 DOI: 10.1515/mr-2023-0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 01/30/2024]
Abstract
Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.
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Affiliation(s)
- Jiaxiao Chen
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Zhonghui Gu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, China
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19
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McGibbon M, Shave S, Dong J, Gao Y, Houston DR, Xie J, Yang Y, Schwaller P, Blay V. From intuition to AI: evolution of small molecule representations in drug discovery. Brief Bioinform 2023; 25:bbad422. [PMID: 38033290 PMCID: PMC10689004 DOI: 10.1093/bib/bbad422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.
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Affiliation(s)
- Miles McGibbon
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Steven Shave
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China
| | - Yumiao Gao
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Douglas R Houston
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Jiancong Xie
- Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Yuedong Yang
- Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vincent Blay
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
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20
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Hu Y, Chen Y, Qin Y, Huang R. Learning entity-oriented representation for biomedical relation extraction. J Biomed Inform 2023; 147:104527. [PMID: 37852347 DOI: 10.1016/j.jbi.2023.104527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 10/11/2023] [Accepted: 10/15/2023] [Indexed: 10/20/2023]
Abstract
Biomedical Relation Extraction (BioRE) aims to automatically extract semantic relations for given entity pairs and is of great significance in biomedical research. Current popular methods often utilize pretrained language models to extract semantic features from individual input instances, which frequently suffer from overlapping semantics. Overlapping semantics refers to the situation in which a sentence contains multiple entity pairs that share the same context, leading to highly similar information between these entity pairs. In this study, we propose a model for learning Entity-oriented Representation (EoR) that aims to improve the performance of the model by enhancing the discriminability between entity pairs. It contains three modules: sentence representation, entity-oriented representation, and output. The first module learns the global semantic information of the input instance; the second module focuses on extracting the semantic information of the sentence from the target entities; and the third module enhances distinguishability among entity pairs and classifies the relation type. We evaluated our approach on four BioRE tasks with eight datasets, and the experiments showed that our EoR achieved state-of-the-art performance for PPI, DDI, CPI, and DPI tasks. Further analysis demonstrated the benefits of entity-oriented semantic information in handling multiple entity pairs in the BioRE task.
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Affiliation(s)
- Ying Hu
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Yanping Chen
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Yongbin Qin
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Ruizhang Huang
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
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21
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Wang Y, Li Z, Rao J, Yang Y, Dai Z. Gene based message passing for drug repurposing. iScience 2023; 26:107663. [PMID: 37670781 PMCID: PMC10475505 DOI: 10.1016/j.isci.2023.107663] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/06/2023] [Accepted: 08/14/2023] [Indexed: 09/07/2023] Open
Abstract
The medicinal effect of a drug acts through a series of genes, and the pathological mechanism of a disease is also related to genes with certain biological functions. However, the complex information between drug or disease and a series of genes is neglected by traditional message passing methods. In this study, we proposed a new framework using two different strategies for gene-drug/disease and drug-disease networks, respectively. We employ long short-term memory (LSTM) network to extract the flow of message from series of genes (gene path) to drug/disease. Incorporating the resulting information of gene paths into drug-disease network, we utilize graph convolutional network (GCN) to predict drug-disease associations. Experimental results showed that our method GeneDR (gene-based drug repurposing) makes better use of the information in gene paths, and performs better in predicting drug-disease associations.
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Affiliation(s)
- Yuxing Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Zhiyang Li
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Jiahua Rao
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
| | - Zhiming Dai
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
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22
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Zhu C, Xia X, Li N, Zhong F, Yang Z, Liu L. RDKG-115: Assisting drug repurposing and discovery for rare diseases by trimodal knowledge graph embedding. Comput Biol Med 2023; 164:107262. [PMID: 37481946 DOI: 10.1016/j.compbiomed.2023.107262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/07/2023] [Accepted: 07/16/2023] [Indexed: 07/25/2023]
Abstract
Rare diseases (RDs) may affect individuals in small numbers, but they have a significant impact on a global scale. Accurate diagnosis of RDs is challenging, and there is a severe lack of drugs available for treatment. Pharmaceutical companies have shown a preference for drug repurposing from existing drugs developed for other diseases due to the high investment, high risk, and long cycle involved in RD drug development. Compared to traditional approaches, knowledge graph embedding (KGE) based methods are more efficient and convenient, as they treat drug repurposing as a link prediction task. KGE models allow for the enrichment of existing knowledge by incorporating multimodal information from various sources. In this study, we constructed RDKG-115, a rare disease knowledge graph involving 115 RDs, composed of 35,643 entities, 25 relations, and 5,539,839 refined triplets, based on 372,384 high-quality literature and 4 biomedical datasets: DRKG, Pathway Commons, PharmKG, and PMapp. Subsequently, we developed a trimodal KGE model containing structure, category, and description embeddings using reverse-hyperplane projection. We utilized this model to infer 4199 reliable new inferred triplets from RDKG-115. Finally, we calculated potential drugs and small molecules for each of the 115 RDs, taking multiple sclerosis as a case study. This study provides a paradigm for large-scale screening of drug repurposing and discovery for RDs, which will speed up the drug development process and ultimately benefit patients with RDs. The source code and data are available at https://github.com/ZhuChaoY/RDKG-115.
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Affiliation(s)
- Chaoyu Zhu
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Xiaoqiong Xia
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Nan Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Fan Zhong
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - Zhihao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
| | - Lei Liu
- Intelligent Medicine Institute, Shanghai Medical College, Fudan University, Shanghai, 200032, China; Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, 200120, China.
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23
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Lin X, Dai L, Zhou Y, Yu ZG, Zhang W, Shi JY, Cao DS, Zeng L, Chen H, Song B, Yu PS, Zeng X. Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction. Brief Bioinform 2023:bbad235. [PMID: 37401373 DOI: 10.1093/bib/bbad235] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/05/2023] Open
Abstract
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.
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Affiliation(s)
- Xuan Lin
- College of Computer Science, Xiangtan University, Xiangtan, China
| | - Lichang Dai
- College of Computer Science, Xiangtan University, Xiangtan, China
| | - Yafang Zhou
- College of Computer Science, Xiangtan University, Xiangtan, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, China
| | - Jian-Yu Shi
- Northwestern Polytechnical University, Xian, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, China
| | - Li Zeng
- AIDD department of Yuyao Biotech, Shanghai, China
| | - Haowen Chen
- College of Computer Science and Electronic Engineering, Hunan University, 410013 Changsha, P. R. China
| | - Bosheng Song
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Philip S Yu
- University of Illinois at Chicago and also holds the Wexler Chair in Information Technology
| | - Xiangxiang Zeng
- College of Information Science and Engineering, Hunan University, Changsha, China
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24
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Abu-Salih B, AL-Qurishi M, Alweshah M, AL-Smadi M, Alfayez R, Saadeh H. Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities. JOURNAL OF BIG DATA 2023; 10:81. [PMID: 37274445 PMCID: PMC10225120 DOI: 10.1186/s40537-023-00774-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 05/17/2023] [Indexed: 06/06/2023]
Abstract
The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.
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Affiliation(s)
| | | | | | - Mohammad AL-Smadi
- Jordan University of Science and Technology, Irbid, Jordan
- Qatar University, Doha, Qatar
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25
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Quan Y, Xiong ZK, Zhang KX, Zhang QY, Zhang W, Zhang HY. Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes. PNAS NEXUS 2023; 2:pgad147. [PMID: 37188275 PMCID: PMC10178923 DOI: 10.1093/pnasnexus/pgad147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/21/2023] [Indexed: 05/17/2023]
Abstract
Identifying promising targets is a critical step in modern drug discovery, with causative genes of diseases that are an important source of successful targets. Previous studies have found that the pathogeneses of various diseases are closely related to the evolutionary events of organisms. Accordingly, evolutionary knowledge can facilitate the prediction of causative genes and further accelerate target identification. With the development of modern biotechnology, massive biomedical data have been accumulated, and knowledge graphs (KGs) have emerged as a powerful approach for integrating and utilizing vast amounts of data. In this study, we constructed an evolution-strengthened knowledge graph (ESKG) and validated applications of ESKG in the identification of causative genes. More importantly, we developed an ESKG-based machine learning model named GraphEvo, which can effectively predict the targetability and the druggability of genes. We further investigated the explainability of the ESKG in druggability prediction by dissecting the evolutionary hallmarks of successful targets. Our study highlights the importance of evolutionary knowledge in biomedical research and demonstrates the potential power of ESKG in promising target identification. The data set of ESKG and the code of GraphEvo can be downloaded from https://github.com/Zhankun-Xiong/GraphEvo.
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Affiliation(s)
| | | | - Ke-Xin Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei 430070, P. R. China
| | - Qing-Ye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei 430070, P. R. China
| | - Wen Zhang
- To whom correspondence should be addressed: ;
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26
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Su C, Hou Y, Zhou M, Rajendran S, Maasch JRA, Abedi Z, Zhang H, Bai Z, Cuturrufo A, Guo W, Chaudhry FF, Ghahramani G, Tang J, Cheng F, Li Y, Zhang R, DeKosky ST, Bian J, Wang F. Biomedical discovery through the integrative biomedical knowledge hub (iBKH). iScience 2023; 26:106460. [PMID: 37020958 PMCID: PMC10068563 DOI: 10.1016/j.isci.2023.106460] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/01/2023] Open
Abstract
The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available.
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Affiliation(s)
- Chang Su
- Department of Health Service Administration and Policy, College of Public Health, Temple University, Philadelphia, PA 19122, USA
| | - Yu Hou
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Manqi Zhou
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Suraj Rajendran
- Tri-Institutional Computational Biology & Medicine Program, Cornell University, New York, NY 10065, USA
| | | | - Zehra Abedi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Winston Guo
- Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Fayzan F. Chaudhry
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Gregory Ghahramani
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec AI Institute and HEC Montreal, Montreal, QC H2S 3H1, Canada
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Yue Li
- School of Computer Science, McGill University, Montreal, QC H3A 0C6, Canada
| | - Rui Zhang
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Steven T. DeKosky
- Department of Neurology, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32610, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
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27
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Chandak P, Huang K, Zitnik M. Building a knowledge graph to enable precision medicine. Sci Data 2023; 10:67. [PMID: 36732524 PMCID: PMC9893183 DOI: 10.1038/s41597-023-01960-3] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 01/11/2023] [Indexed: 02/04/2023] Open
Abstract
Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. PrimeKG contains an abundance of 'indications', 'contradictions', and 'off-label use' drug-disease edges that lack in other knowledge graphs and can support AI analyses of how drugs affect disease-associated networks. We supplement PrimeKG's graph structure with language descriptions of clinical guidelines to enable multimodal analyses and provide instructions for continual updates of PrimeKG as new data become available.
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Affiliation(s)
- Payal Chandak
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, 02139, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, 02115, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Harvard Data Science Initiative, Cambridge, MA, 02138, USA.
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28
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Li J, Gao J, Feng B, Jing Y. PlagueKD: a knowledge graph-based plague knowledge database. Database (Oxford) 2022; 2022:baac100. [PMID: 36412326 PMCID: PMC10161524 DOI: 10.1093/database/baac100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/17/2022] [Accepted: 10/28/2022] [Indexed: 11/23/2022]
Abstract
Plague has been confirmed as an extremely horrific international quarantine infectious disease attributed to Yersinia pestis. It has an extraordinarily high lethal rate that poses a serious hazard to human and animal lives. With the deepening of research, there has been a considerable amount of literature related to the plague that has never been systematically integrated. Indeed, it makes researchers time-consuming and laborious when they conduct some investigation. Accordingly, integrating and excavating plague-related knowledge from considerable literature takes on a critical significance. Moreover, a comprehensive plague knowledge base should be urgently built. To solve the above issues, the plague knowledge base is built for the first time. A database is built from the literature mining based on knowledge graph, which is capable of storing, retrieving, managing and accessing data. First, 5388 plague-related abstracts that were obtained automatically from PubMed are integrated, and plague entity dictionary and ontology knowledge base are constructed by using text mining technology. Second, the scattered plague-related knowledge is correlated through knowledge graph technology. A multifactor correlation knowledge graph centered on plague is formed, which contains 9633 nodes of 33 types (e.g. disease, gene, protein, species, symptom, treatment and geographic location), as well as 9466 association relations (e.g. disease-gene, gene-protein and disease-species). The Neo4j graph database is adopted to store and manage the relational data in the form of triple. Lastly, a plague knowledge base is built, which can successfully manage and visualize a large amount of structured plague-related data. This knowledge base almost provides an integrated and comprehensive plague-related knowledge. It should not only help researchers to better understand the complex pathogenesis and potential therapeutic approaches of plague but also take on a key significance to reference for exploring potential action mechanisms of corresponding drug candidates and the development of vaccine in the future. Furthermore, it is of great significance to promote the field of plague research. Researchers are enabled to acquire data more easily for more effective research. Database URL: http://39.104.28.169:18095/.
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Affiliation(s)
- Jin Li
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot 010011, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, Inner Mongolia Autonomous Region 010018, China
| | - Jing Gao
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot 010011, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, Inner Mongolia Autonomous Region 010018, China
| | - Baiyang Feng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Erdos East Street No. 29, Hohhot 010011, China
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot, Inner Mongolia Autonomous Region 010018, China
| | - Yi Jing
- Faculty of Science, University of New South Wales, Sydney, New Sales Wales 2020, Australia
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29
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Tian Z, Peng X, Fang H, Zhang W, Dai Q, Ye Y. MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms. Brief Bioinform 2022; 23:6761042. [PMID: 36242566 DOI: 10.1093/bib/bbac434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/19/2022] [Accepted: 09/08/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Discovering the drug-target interactions (DTIs) is a crucial step in drug development such as the identification of drug side effects and drug repositioning. Since identifying DTIs by web-biological experiments is time-consuming and costly, many computational-based approaches have been proposed and have become an efficient manner to infer the potential interactions. Although extensive effort is invested to solve this task, the prediction accuracy still needs to be improved. More especially, heterogeneous network-based approaches do not fully consider the complex structure and rich semantic information in these heterogeneous networks. Therefore, it is still a challenge to predict DTIs efficiently. RESULTS In this study, we develop a novel method via Multiview heterogeneous information network embedding with Hierarchical Attention mechanisms to discover potential Drug-Target Interactions (MHADTI). Firstly, MHADTI constructs different similarity networks for drugs and targets by utilizing their multisource information. Combined with the known DTI network, three drug-target heterogeneous information networks (HINs) with different views are established. Secondly, MHADTI learns embeddings of drugs and targets from multiview HINs with hierarchical attention mechanisms, which include the node-level, semantic-level and graph-level attentions. Lastly, MHADTI employs the multilayer perceptron to predict DTIs with the learned deep feature representations. The hierarchical attention mechanisms could fully consider the importance of nodes, meta-paths and graphs in learning the feature representations of drugs and targets, which makes their embeddings more comprehensively. Extensive experimental results demonstrate that MHADTI performs better than other SOTA prediction models. Moreover, analysis of prediction results for some interested drugs and targets further indicates that MHADTI has advantages in discovering DTIs. AVAILABILITY AND IMPLEMENTATION https://github.com/pxystudy/MHADTI.
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Affiliation(s)
- Zhen Tian
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Xiangyu Peng
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Haichuan Fang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Wenjie Zhang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
| | - Qiguo Dai
- School of Computer Science and Engineering, Dalian Minzu University, Dalian,116600, China
| | - Yangdong Ye
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
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30
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Bonner S, Barrett IP, Ye C, Swiers R, Engkvist O, Bender A, Hoyt CT, Hamilton WL. A review of biomedical datasets relating to drug discovery: a knowledge graph perspective. Brief Bioinform 2022; 23:6712301. [PMID: 36151740 DOI: 10.1093/bib/bbac404] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/14/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022] Open
Abstract
Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.
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Affiliation(s)
- Stephen Bonner
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Ian P Barrett
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Cheng Ye
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Rowan Swiers
- Data Sciences and Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweeden
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, UK
| | | | - William L Hamilton
- School of Computer Science, McGill University, Canada.,Mila-Quebec AI Institute, Montreal, Canada
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31
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Rivas-Barragan D, Domingo-Fernández D, Gadiya Y, Healey D. Ensembles of knowledge graph embedding models improve predictions for drug discovery. Brief Bioinform 2022; 23:6831005. [PMID: 36384050 PMCID: PMC9677479 DOI: 10.1093/bib/bbac481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/04/2022] [Accepted: 10/08/2022] [Indexed: 11/18/2022] Open
Abstract
Recent advances in Knowledge Graphs (KGs) and Knowledge Graph Embedding Models (KGEMs) have led to their adoption in a broad range of fields and applications. The current publishing system in machine learning requires newly introduced KGEMs to achieve state-of-the-art performance, surpassing at least one benchmark in order to be published. Despite this, dozens of novel architectures are published every year, making it challenging for users, even within the field, to deduce the most suitable configuration for a given application. A typical biomedical application of KGEMs is drug-disease prediction in the context of drug discovery, in which a KGEM is trained to predict triples linking drugs and diseases. These predictions can be later tested in clinical trials following extensive experimental validation. However, given the infeasibility of evaluating each of these predictions and that only a minimal number of candidates can be experimentally tested, models that yield higher precision on the top prioritized triples are preferred. In this paper, we apply the concept of ensemble learning on KGEMs for drug discovery to assess whether combining the predictions of several models can lead to an overall improvement in predictive performance. First, we trained and benchmarked 10 KGEMs to predict drug-disease triples on two independent biomedical KGs designed for drug discovery. Following, we applied different ensemble methods that aggregate the predictions of these models by leveraging the distribution or the position of the predicted triple scores. We then demonstrate how the ensemble models can achieve better results than the original KGEMs by benchmarking the precision (i.e., number of true positives prioritized) of their top predictions. Lastly, we released the source code presented in this work at https://github.com/enveda/kgem-ensembles-in-drug-discovery.
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Affiliation(s)
| | - Daniel Domingo-Fernández
- Corresponding author: Daniel Domingo-Fernández, Department of Data Science. Enveda Biosciences, Boulder, CO, USA. E-mail:
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32
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Quantitative evaluation of explainable graph neural networks for molecular property prediction. PATTERNS (NEW YORK, N.Y.) 2022; 3:100628. [PMID: 36569553 PMCID: PMC9782255 DOI: 10.1016/j.patter.2022.100628] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 08/09/2022] [Accepted: 10/12/2022] [Indexed: 11/12/2022]
Abstract
Graph neural networks (GNNs) have received increasing attention because of their expressive power on topological data, but they are still criticized for their lack of interpretability. To interpret GNN models, explainable artificial intelligence (XAI) methods have been developed. However, these methods are limited to qualitative analyses without quantitative assessments from the real-world datasets due to a lack of ground truths. In this study, we have established five XAI-specific molecular property benchmarks, including two synthetic and three experimental datasets. Through the datasets, we quantitatively assessed six XAI methods on four GNN models and made comparisons with seven medicinal chemists of different experience levels. The results demonstrated that XAI methods could deliver reliable and informative answers for medicinal chemists in identifying the key substructures. Moreover, the identified substructures were shown to complement existing classical fingerprints to improve molecular property predictions, and the improvements increased with the growth of training data.
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33
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Hong Y, Luo P, Jin S, Liu X. LaGAT: link-aware graph attention network for drug-drug interaction prediction. Bioinformatics 2022; 38:5406-5412. [PMID: 36271850 PMCID: PMC9750103 DOI: 10.1093/bioinformatics/btac682] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/30/2022] [Accepted: 10/21/2022] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical databases, large-scale biological knowledge graphs containing drug information have been widely used for DDI prediction. However, large knowledge graphs inevitably suffer from data noise problems, which limit the performance and interpretability of models based on the knowledge graph. Recent studies attempt to improve models by introducing inductive bias through an attention mechanism. However, they all only depend on the topology of entity nodes independently to generate fixed attention pathways, without considering the semantic diversity of entity nodes in different drug pair links. This makes it difficult for models to select more meaningful nodes to overcome data quality limitations and make more interpretable predictions. RESULTS To address this issue, we propose a Link-aware Graph Attention method for DDI prediction, called LaGAT, which is able to generate different attention pathways for drug entities based on different drug pair links. For a drug pair link, the LaGAT uses the embedding representation of one of the drugs as a query vector to calculate the attention weights, thereby selecting the appropriate topological neighbor nodes to obtain the semantic information of the other drug. We separately conduct experiments on binary and multi-class classification and visualize the attention pathways generated by the model. The results prove that LaGAT can better capture semantic relationships and achieves remarkably superior performance over both the classical and state-of-the-art models on DDI prediction. AVAILABILITYAND IMPLEMENTATION The source code and data are available at https://github.com/Azra3lzz/LaGAT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Shuting Jin
- School of Informatics, Xiamen University, Xiamen 361005, China,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China,MindRank AI Ltd., Hangzhou 310000, China
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Gao Z, Ding P, Xu R. KG-Predict: A knowledge graph computational framework for drug repurposing. J Biomed Inform 2022; 132:104133. [PMID: 35840060 PMCID: PMC9595135 DOI: 10.1016/j.jbi.2022.104133] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 06/18/2022] [Accepted: 07/03/2022] [Indexed: 11/26/2022]
Abstract
The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106 OH, USA.
| | - Pingjian Ding
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106 OH, USA.
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106 OH, USA.
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35
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Asaad C, Ghogho M. AsthmaKGxE: An asthma-environment interaction knowledge graph leveraging public databases and scientific literature. Comput Biol Med 2022; 148:105933. [DOI: 10.1016/j.compbiomed.2022.105933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 06/11/2022] [Accepted: 07/30/2022] [Indexed: 11/03/2022]
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Königs C, Friedrichs M, Dietrich T. The heterogeneous pharmacological medical biochemical network PharMeBINet. Sci Data 2022; 9:393. [PMID: 35821017 PMCID: PMC9276653 DOI: 10.1038/s41597-022-01510-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/22/2022] [Indexed: 12/04/2022] Open
Abstract
Heterogeneous biomedical pharmacological databases are important for multiple fields in bioinformatics. Hetionet is a freely available database combining diverse entities and relationships from 29 public resources. Therefore, it is used as the basis for this project. 19 additional pharmacological medical and biological databases such as CTD, DrugBank, and ClinVar are parsed and integrated into Neo4j. Afterwards, the information is merged into the Hetionet structure. Different mapping methods are used such as external identification systems or name mapping. The resulting open-source Neo4j database PharMeBINet has 2,869,407 different nodes with 66 labels and 15,883,653 relationships with 208 edge types. It is a heterogeneous database containing interconnected information on ADRs, diseases, drugs, genes, gene variations, proteins, and more. Relationships between these entities represent drug-drug interactions or drug-causes-ADR relations, to name a few. It has much potential for developing further data analyses including machine learning applications. A web application for accessing the database is free to use for everyone and available at https://pharmebi.net. Additionally, the database is deposited on Zenodo at 10.5281/zenodo.6578218. Measurement(s) | data integration objective | Technology Type(s) | database creation objective |
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Affiliation(s)
- Cassandra Königs
- Bielefeld University, Bioinformatics/Medical Informatics Department, Bielefeld, 33615, Germany.
| | - Marcel Friedrichs
- Bielefeld University, Bioinformatics/Medical Informatics Department, Bielefeld, 33615, Germany
| | - Theresa Dietrich
- Bielefeld University, Bioinformatics/Medical Informatics Department, Bielefeld, 33615, Germany
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Schutte D, Vasilakes J, Bompelli A, Zhou Y, Fiszman M, Xu H, Kilicoglu H, Bishop JR, Adam T, Zhang R. Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature. J Biomed Inform 2022; 131:104120. [PMID: 35709900 PMCID: PMC9335448 DOI: 10.1016/j.jbi.2022.104120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 04/26/2022] [Accepted: 06/08/2022] [Indexed: 12/04/2022]
Abstract
Objective: Develop a novel methodology to create a comprehensive knowledge graph (SuppKG) to represent a domain with limited coverage in the Unified Medical Language System (UMLS), specifically dietary supplement (DS) information for discovering drug-supplement interactions (DSI), by leveraging biomedical natural language processing (NLP) technologies and a DS domain terminology. Materials and Methods: We created SemRepDS (an extension of an NLP tool, SemRep), capable of extracting semantic relations from abstracts by leveraging a DS-specific terminology (iDISK) containing 28,884 DS terms not found in the UMLS. PubMed abstracts were processed using SemRepDS to generate semantic relations, which were then filtered using a PubMedBERT model to remove incorrect relations before generating SuppKG. Two discovery pathways were applied to SuppKG to identify potential DSIs, which are then compared with an existing DSI database and also evaluated by medical professionals for mechanistic plausibility. Results: SemRepDS returned 158.5% more DS entities and 206.9% more DS relations than SemRep. The fine-tuned PubMedBERT model (significantly outperformed other machine learning and BERT models) obtained an F1 score of 0.8605 and removed 43.86% of semantic relations, improving the precision of the relations by 26.4% over pre-filtering. SuppKG consists of 56,635 nodes and 595,222 directed edges with 2,928 DS-specific nodes and 164,738 edges. Manual review of findings identified 182 of 250 (72.8%) proposed DS-Gene-Drug and 77 of 100 (77%) proposed DS-Gene1-Function-Gene2-Drug pathways to be mechanistically plausible. Discussion: With added DS terminology to the UMLS, SemRepDS has the capability to find more DS-specific semantic relationships from PubMed than SemRep. The utility of the resulting SuppKG was demonstrated using discovery patterns to find novel DSIs. Conclusion: For the domain with limited coverage in the traditional terminology (e.g., UMLS), we demonstrated an approach to leverage domain terminology and improve existing NLP tools to generate a more comprehensive knowledge graph for the downstream task. Even this study focuses on DSI, the method may be adapted to other domains.
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Affiliation(s)
- Dalton Schutte
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA
| | - Jake Vasilakes
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA; National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, United Kingdom
| | - Anu Bompelli
- Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA
| | - Yuqi Zhou
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA
| | - Marcelo Fiszman
- NITES - Núcleo de Inovação e Tecnologia Em Saúde, Pontifical Catholic University of Rio de Janeiro, Brazil
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Halil Kilicoglu
- School of Information Sciences, University of Illinois, Champaign, IL, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Terrence Adam
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA
| | - Rui Zhang
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA; Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, USA.
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Zhu X, Gu Y, Xiao Z. HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning. Front Genet 2022; 13:799349. [PMID: 35571049 PMCID: PMC9091197 DOI: 10.3389/fgene.2022.799349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
Recent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific findings have not been systematically organized, affecting the efficiency of knowledge discovery and usage. Existing knowledge graphs in herbalism mainly focus on diagnosis and treatment with an absence of knowledge connection with molecular medicine. To fill this gap, we present HerbKG, a knowledge graph that bridges herbal and molecular medicine. The core bio-entities of HerbKG include herbs, chemicals extracted from the herbs, genes that are affected by the chemicals, and diseases treated by herbs due to the functions of genes. We have developed a learning framework to automate the process of HerbKG construction. The resulting HerbKG, after analyzing over 500K PubMed abstracts, is populated with 53K relations, providing extensive herbal-molecular domain knowledge in support of downstream applications. The code and an interactive tool are available at https://github.com/FeiYee/HerbKG.
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Affiliation(s)
- Xian Zhu
- School of Information Management, Nanjing University, Nanjing, China
- School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yueming Gu
- School of Computing and Information Systems, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC, Australia
| | - Zhifeng Xiao
- School of Engineering, Penn State Erie, The Behrend College, Erie, PA, United States
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39
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Zhu C, Yang Z, Xia X, Li N, Zhong F, Liu L. Multimodal reasoning based on knowledge graph embedding for specific diseases. Bioinformatics 2022; 38:2235-2245. [PMID: 35150235 PMCID: PMC9004655 DOI: 10.1093/bioinformatics/btac085] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/06/2022] [Accepted: 02/07/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Knowledge Graph (KG) is becoming increasingly important in the biomedical field. Deriving new and reliable knowledge from existing knowledge by KG embedding technology is a cutting-edge method. Some add a variety of additional information to aid reasoning, namely multimodal reasoning. However, few works based on the existing biomedical KGs are focused on specific diseases. RESULTS This work develops a construction and multimodal reasoning process of Specific Disease Knowledge Graphs (SDKGs). We construct SDKG-11, a SDKG set including five cancers, six non-cancer diseases, a combined Cancer5 and a combined Diseases11, aiming to discover new reliable knowledge and provide universal pre-trained knowledge for that specific disease field. SDKG-11 is obtained through original triplet extraction, standard entity set construction, entity linking and relation linking. We implement multimodal reasoning by reverse-hyperplane projection for SDKGs based on structure, category and description embeddings. Multimodal reasoning improves pre-existing models on all SDKGs using entity prediction task as the evaluation protocol. We verify the model's reliability in discovering new knowledge by manually proofreading predicted drug-gene, gene-disease and disease-drug pairs. Using embedding results as initialization parameters for the biomolecular interaction classification, we demonstrate the universality of embedding models. AVAILABILITY AND IMPLEMENTATION The constructed SDKG-11 and the implementation by TensorFlow are available from https://github.com/ZhuChaoY/SDKG-11. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chaoyu Zhu
- Institute of Biomedical Sciences and School of Basic Medical Science, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Zhihao Yang
- College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xiaoqiong Xia
- Institute of Biomedical Sciences and School of Basic Medical Science, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Nan Li
- College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Fan Zhong
- To whom correspondence should be addressed. or
| | - Lei Liu
- To whom correspondence should be addressed. or
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40
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Ye Q, Hsieh CY, Yang Z, Kang Y, Chen J, Cao D, He S, Hou T. A unified drug-target interaction prediction framework based on knowledge graph and recommendation system. Nat Commun 2021; 12:6775. [PMID: 34811351 PMCID: PMC8635420 DOI: 10.1038/s41467-021-27137-3] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 11/05/2021] [Indexed: 02/06/2023] Open
Abstract
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.
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Affiliation(s)
- Qing Ye
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang China ,grid.13402.340000 0004 1759 700XCollege of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang China ,grid.13402.340000 0004 1759 700XState Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058 China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory, Shenzhen, 518057 Guangdong China
| | - Ziyi Yang
- Tencent Quantum Laboratory, Shenzhen, 518057 Guangdong China
| | - Yu Kang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang China
| | - Jiming Chen
- grid.13402.340000 0004 1759 700XCollege of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, China.
| | - Shibo He
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China. .,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
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41
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Wang J, Liu X, Shen S, Deng L, Liu H. DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations. Brief Bioinform 2021; 23:6375262. [PMID: 34571537 DOI: 10.1093/bib/bbab390] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/14/2021] [Accepted: 08/28/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experiments. Therefore, computational screening has become an important way to prioritize drug combinations. Graph neural network has recently shown remarkable performance in the prediction of compound-protein interactions, but it has not been applied to the screening of drug combinations. RESULTS In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The feature embeddings of drug molecule structure and gene expression profiles were taken as input to multilayer feedforward neural network to identify the synergistic drug combinations. We compared DeepDDS (Deep Learning for Drug-Drug Synergy prediction) with classical machine learning methods and other deep learning-based methods on benchmark data set, and the leave-one-out experimental results showed that DeepDDS achieved better performance than competitive methods. Also, on an independent test set released by well-known pharmaceutical enterprise AstraZeneca, DeepDDS was superior to competitive methods by more than 16% predictive precision. Furthermore, we explored the interpretability of the graph attention network and found the correlation matrix of atomic features revealed important chemical substructures of drugs. We believed that DeepDDS is an effective tool that prioritized synergistic drug combinations for further wet-lab experiment validation. AVAILABILITY AND IMPLEMENTATION Source code and data are available at https://github.com/Sinwang404/DeepDDS/tree/master.
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Affiliation(s)
- Jinxian Wang
- Hunan Agricultural University in 2019, and at present is studying for a Master's degree at Central South University, China
| | - Xuejun Liu
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
| | - Siyuan Shen
- School of Software, Xinjiang University, Urumqi, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hui Liu
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
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42
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Wang X, Yang Y, Li K, Li W, Li F, Peng S. BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions. Bioinformatics 2021; 37:4793-4800. [PMID: 34329382 DOI: 10.1093/bioinformatics/btab565] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 07/18/2021] [Accepted: 07/29/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Predicting entity relationship can greatly benefit important biomedical problems. Recently, a large amount of biomedical heterogeneous networks (BioHNs) are generated and offer opportunities for developing network-based learning approaches to predict relationships among entities. However, current researches slightly explored BioHNs-based self-supervised representation learning methods, and are hard to simultaneously capturing local- and global-level association information among entities. RESULTS In this study, we propose a biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions, termed BioERP. A self-supervised meta path detection mechanism is proposed to train a deep Transformer encoder model that can capture the global structure and semantic feature in BioHNs. Meanwhile, a biomedical entity mask learning strategy is designed to reflect local associations of vertices. Finally, the representations from different task models are concatenated to generate two-level representation vectors for predicting relationships among entities. The results on eight datasets show BioERP outperforms 30 state-of-the-art methods. In particular, BioERP reveals great performance with results close to 1 in terms of AUC and AUPR on the drug-target interaction predictions. In summary, BioERP is a promising bio-entity relationship prediction approach. AVAILABILITY Source code and data can be downloaded from https://github.com/pengsl-lab/BioERP.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Yaning Yang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Kenli Li
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Wentao Li
- School of Computer Science, National University of Defense Technology, Changsha, 410073, China
| | - Fei Li
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100850, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.,School of Computer Science, National University of Defense Technology, Changsha, 410073, China.,Peng Cheng Lab, Shenzhen 518000, China
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Wang H, Xi Q, Liang P, Zheng L, Hong Y, Zuo Y. IHEC_RAAC: a online platform for identifying human enzyme classes via reduced amino acid cluster strategy. Amino Acids 2021; 53:239-251. [PMID: 33486591 DOI: 10.1007/s00726-021-02941-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 01/11/2021] [Indexed: 12/18/2022]
Abstract
Enzymes have been proven to play considerable roles in disease diagnosis and biological functions. The feature extraction that truly reflects the intrinsic properties of protein is the most critical step for the automatic identification of enzymes. Although lots of feature extraction methods have been proposed, some challenges remain. In this study, we developed a predictor called IHEC_RAAC, which has the capability to identify whether a protein is a human enzyme and distinguish the function of the human enzyme. To improve the feature representation ability, protein sequences were encoded by a new feature-vector called 'reduced amino acid cluster'. We calculated 673 amino acid reduction alphabets to determine the optimal feature representative scheme. The tenfold cross-validation test showed that the accuracy of IHEC_RAAC to identify human enzymes was 74.66% and further discriminate the human enzyme classes with an accuracy of 54.78%, which was 2.06% and 8.68% higher than the state-of-the-art predictors, respectively. Additionally, the results from the independent dataset indicated that IHEC_RAAC can effectively predict human enzymes and human enzyme classes to further provide guidance for protein research. A user-friendly web server, IHEC_RAAC, is freely accessible at http://bioinfor.imu.edu.cn/ihecraac .
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Affiliation(s)
- Hao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Qilemuge Xi
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yan Hong
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
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Xu B, Liu D, Wang Z, Tian R, Zuo Y. Multi-substrate selectivity based on key loops and non-homologous domains: new insight into ALKBH family. Cell Mol Life Sci 2021; 78:129-141. [PMID: 32642789 PMCID: PMC11072825 DOI: 10.1007/s00018-020-03594-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/24/2020] [Accepted: 07/03/2020] [Indexed: 12/16/2022]
Abstract
AlkB homologs (ALKBH) are a family of specific demethylases that depend on Fe2+ and α-ketoglutarate to catalyze demethylation on different substrates, including ssDNA, dsDNA, mRNA, tRNA, and proteins. Previous studies have made great progress in determining the sequence, structure, and molecular mechanism of the ALKBH family. Here, we first review the multi-substrate selectivity of the ALKBH demethylase family from the perspective of sequence and structural evolution. The construction of the phylogenetic tree and the comparison of key loops and non-homologous domains indicate that the paralogs with close evolutionary relationship have similar domain compositions. The structures show that the lack and variations of four key loops change the shape of clefts to cause the differences in substrate affinity, and non-homologous domains may be related to the compatibility of multiple substrates. We anticipate that the new insights into selectivity determinants of the ALKBH family are useful for understanding the demethylation mechanisms.
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Affiliation(s)
- Baofang Xu
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Dongyang Liu
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
- Key Laboratory of Photobiology, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zerong Wang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Ruixia Tian
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yongchun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
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