Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information

Overview

Journal Interdiscip Sci

Specialty Biology

Date 2022 Apr 8

PMID 35391615

Authors

Zhanchao Li

Mengru Wang

Dongdong Peng

Jie Liu

Yun Xie

Zong Dai

Xiaoyong Zou

Affiliations

Soon will be listed here.

Abstract

The identification of chemical-disease association types is helpful not only to discovery lead compounds and study drug repositioning, but also to treat disease and decipher pathomechanism. It is very urgent to develop computational method for identifying potential chemical-disease association types, since wet methods are usually expensive, laborious and time-consuming. In this study, molecular fingerprint, gene ontology and pathway are utilized to characterize chemicals and diseases. A novel predictor is proposed to recognize potential chemical-disease associations at the first layer, and further distinguish whether their relationships belong to biomarker or therapeutic relations at the second layer. The prediction performance of current method is assessed using the benchmark dataset based on ten-fold cross-validation. The practical prediction accuracies of the first layer and the second layer are 78.47% and 72.07%, respectively. The recognition ability for lead compounds, new drug indications, potential and true chemical-disease association pairs has also been investigated and confirmed by constructing a variety of datasets and performing a series of experiments. It is anticipated that the current method can be considered as a powerful high-throughput virtual screening tool for drug researches and developments.

References

Scannell J, Blanckley A, Boldon H, Warrington B . Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. 2012; 11(3):191-200. DOI: 10.1038/nrd3681. View

DiMasi J, Feldman L, Seckler A, Wilson A . Trends in risks associated with new drug development: success rates for investigational drugs. Clin Pharmacol Ther. 2010; 87(3):272-7. DOI: 10.1038/clpt.2009.295. View

Neveol A, Islamaj Dogan R, Lu Z . Semi-automatic semantic annotation of PubMed queries: a study on quality, efficiency, satisfaction. J Biomed Inform. 2010; 44(2):310-8. PMC: 3063330. DOI: 10.1016/j.jbi.2010.11.001. View

Kim S, Chen J, Cheng T, Gindulyte A, He J, He S . PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2018; 47(D1):D1102-D1109. PMC: 6324075. DOI: 10.1093/nar/gky1033. View

Lowe D, OBoyle N, Sayle R . Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall. Database (Oxford). 2016; 2016. PMC: 4825350. DOI: 10.1093/database/baw039. View

Peng Y, Wei C, Lu Z . Improving chemical disease relation extraction with rich features and weakly labeled data. J Cheminform. 2017; 8:53. PMC: 5054544. DOI: 10.1186/s13321-016-0165-z. View

Pons E, Becker B, Akhondi S, Afzal Z, van Mulligen E, Kors J . Extraction of chemical-induced diseases using prior knowledge and textual information. Database (Oxford). 2016; 2016. PMC: 4831722. DOI: 10.1093/database/baw046. View

Xu J, Wu Y, Zhang Y, Wang J, Lee H, Xu H . CD-REST: a system for extracting chemical-induced disease relation in literature. Database (Oxford). 2016; 2016. PMC: 4808251. DOI: 10.1093/database/baw036. View

Alam F, Corazza A, Lavelli A, Zanoli R . A knowledge-poor approach to chemical-disease relation extraction. Database (Oxford). 2016; 2016. PMC: 4869795. DOI: 10.1093/database/baw071. View

10.

Gu J, Qian L, Zhou G . Chemical-induced disease relation extraction with various linguistic features. Database (Oxford). 2016; 2016. PMC: 4822558. DOI: 10.1093/database/baw042. View

11.

Onye S, Akkeles A, Dimililer N . relSCAN - A system for extracting chemical-induced disease relation from biomedical literature. J Biomed Inform. 2018; 87:79-87. DOI: 10.1016/j.jbi.2018.09.018. View

12.

Li H, Yang M, Chen Q, Tang B, Wang X, Yan J . Chemical-induced disease extraction via recurrent piecewise convolutional neural networks. BMC Med Inform Decis Mak. 2018; 18(Suppl 2):60. PMC: 6069297. DOI: 10.1186/s12911-018-0629-3. View

13.

Chen T, Wu M, Li H . A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning. Database (Oxford). 2019; 2019. PMC: 6892305. DOI: 10.1093/database/baz116. View

14.

Mitra S, Saha S, Hasanuzzaman M . A Multi-View Deep Neural Network Model for Chemical-Disease Relation Extraction From Imbalanced Datasets. IEEE J Biomed Health Inform. 2020; 24(11):3315-3325. DOI: 10.1109/JBHI.2020.2983365. View

15.

Zheng W, Lin H, Li Z, Liu X, Li Z, Xu B . An effective neural model extracting document level chemical-induced disease relations from biomedical literature. J Biomed Inform. 2018; 83:1-9. DOI: 10.1016/j.jbi.2018.05.001. View

16.

Zhou H, Ning S, Yang Y, Liu Z, Lang C, Lin Y . Chemical-induced disease relation extraction with dependency information and prior knowledge. J Biomed Inform. 2018; 84:171-178. DOI: 10.1016/j.jbi.2018.07.007. View

17.

Zhou H, Deng H, Chen L, Yang Y, Jia C, Huang D . Exploiting syntactic and semantics information for chemical-disease relation extraction. Database (Oxford). 2016; 2016. PMC: 4831723. DOI: 10.1093/database/baw048. View

18.

Wang J, Chen X, Zhang Y, Zhang Y, Wen J, Lin H . Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation. JMIR Med Inform. 2020; 8(7):e17638. PMC: 7458061. DOI: 10.2196/17638. View

19.

Lu H, Li L, Li Z, Zhao S . Extracting chemical-induced disease relation by integrating a hierarchical concentrative attention and a hybrid graph-based neural network. J Biomed Inform. 2021; 121:103874. DOI: 10.1016/j.jbi.2021.103874. View

20.

Kim S, Thiessen P, Bolton E, Chen J, Fu G, Gindulyte A . PubChem Substance and Compound databases. Nucleic Acids Res. 2015; 44(D1):D1202-13. PMC: 4702940. DOI: 10.1093/nar/gkv951. View