An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network
Abstract Background Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discov...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-01-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-04553-2 |
| _version_ | 1828145305011355648 |
|---|---|
| author | Hanjing Jiang Yabing Huang |
| author_facet | Hanjing Jiang Yabing Huang |
| author_sort | Hanjing Jiang |
| collection | DOAJ |
| description | Abstract Background Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. Results In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. Conclusions The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition. |
| first_indexed | 2024-04-11T20:31:00Z |
| format | Article |
| id | doaj.art-faa6a6f7af624c37a947e43b9da5df50 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-11T20:31:00Z |
| publishDate | 2022-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-faa6a6f7af624c37a947e43b9da5df502022-12-22T04:04:31ZengBMCBMC Bioinformatics1471-21052022-01-0123111710.1186/s12859-021-04553-2An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular networkHanjing Jiang0Yabing Huang1Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, Institute of Artificial Intelligence, School of Artificial Intelligence and Automation, Huazhong University of Science and TechnologyDepartment of Pathology, Renmin Hospital of Wuhan UniversityAbstract Background Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. Results In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. Conclusions The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.https://doi.org/10.1186/s12859-021-04553-2Drug-disease associationGraph representation learningMulti-biomolecular network |
| spellingShingle | Hanjing Jiang Yabing Huang An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network BMC Bioinformatics Drug-disease association Graph representation learning Multi-biomolecular network |
| title | An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network |
| title_full | An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network |
| title_fullStr | An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network |
| title_full_unstemmed | An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network |
| title_short | An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network |
| title_sort | effective drug disease associations prediction model based on graphic representation learning over multi biomolecular network |
| topic | Drug-disease association Graph representation learning Multi-biomolecular network |
| url | https://doi.org/10.1186/s12859-021-04553-2 |
| work_keys_str_mv | AT hanjingjiang aneffectivedrugdiseaseassociationspredictionmodelbasedongraphicrepresentationlearningovermultibiomolecularnetwork AT yabinghuang aneffectivedrugdiseaseassociationspredictionmodelbasedongraphicrepresentationlearningovermultibiomolecularnetwork AT hanjingjiang effectivedrugdiseaseassociationspredictionmodelbasedongraphicrepresentationlearningovermultibiomolecularnetwork AT yabinghuang effectivedrugdiseaseassociationspredictionmodelbasedongraphicrepresentationlearningovermultibiomolecularnetwork |