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Luo ZH, Zhu LD, Wang YM, Hu Qian S, Li M, Zhang W, Chen ZX. DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining. Brief Bioinform 2022; 23:6605028. [PMID: 35679594 DOI: 10.1093/bib/bbac228] [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: 02/02/2022] [Revised: 05/09/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Disease pathogenesis is always a major topic in biomedical research. With the exponential growth of biomedical information, drug effect analysis for specific phenotypes has shown great promise in uncovering disease-associated pathways. However, this method has only been applied to a limited number of drugs. Here, we extracted the data of 4634 diseases, 3671 drugs, 112 809 disease-drug associations and 81 527 drug-gene associations by text mining of 29 168 919 publications. On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method. Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes. In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM. Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.
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Affiliation(s)
- Zhi-Hui Luo
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China
| | - Li-Da Zhu
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China
| | - Ya-Min Wang
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China
| | - Sheng Hu Qian
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China
| | - Menglu Li
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China
| | - Zhen-Xia Chen
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China.,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, Hubei, PR China
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Lu L, Qin J, Chen J, Wu H, Zhao Q, Miyano S, Zhang Y, Yu H, Li C. DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations. Front Pharmacol 2022; 12:772026. [PMID: 35126114 PMCID: PMC8809407 DOI: 10.3389/fphar.2021.772026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/19/2021] [Indexed: 12/20/2022] Open
Abstract
Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases; the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs), and contraindications (CIs). Incorporating these different clinical phenotypes of drug–disease associations (DDAs) can improve the prediction accuracy of the DDAs. Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor that supports drug repositioning by submitting known Is, SEs, and CIs for a target drug of interest. The dataset for Is, SEs, and CIs was extracted from PREDICT, SIDER, and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional restricted Boltzmann machine (RBM) enabling the identification of different phenotypes of drug–disease associations, including the prediction of as yet unknown DDAs in the input. Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the drug–disease association network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-nearest neighbors method (KNN, including drug-based KNN and disease-based KNN), Random Forest, and XGBoost. By conducting leave-one-drug-class-out cross-validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects, and contraindications, 7/10, 9/10, and 9/10 hit known drug–disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug–disease associations.
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Affiliation(s)
- Lu Lu
- Department of Human Genetics, Department of Ultrasound and Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiale Qin
- Department of Human Genetics, Department of Ultrasound and Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, China
| | - Jiandong Chen
- School of Public Health, Undergraduate School of Zhejiang University, Hangzhou, China
| | - Hao Wu
- Department of Human Genetics, Department of Ultrasound and Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiang Zhao
- Department of Human Genetics, Department of Ultrasound and Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yaozhong Zhang
- The Institute of Medical Science, the University of Tokyo, Tokyo, Japan
- *Correspondence: Yaozhong Zhang, ; Hua Yu, ; Chen Li,
| | - Hua Yu
- Department of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Yaozhong Zhang, ; Hua Yu, ; Chen Li,
| | - Chen Li
- Department of Human Genetics, Department of Ultrasound and Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, China
- Department of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Yaozhong Zhang, ; Hua Yu, ; Chen Li,
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