SemaTyP: a knowledge graph based literature mining method for drug discovery. Sang, S., Yang, Z., Wang, L., Liu, X., Lin, H., & Wang, J. BMC Bioinformatics, May, 2018.
SemaTyP: a knowledge graph based literature mining method for drug discovery [link]Paper  doi  abstract   bibtex   
Background Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Methods Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies’ existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. Results The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. Conclusions In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods. Electronic supplementary material The online version of this article (10.1186/s12859-018-2167-5) contains supplementary material, which is available to authorized users.
@article{sang_sematyp:_2018,
	title = {{SemaTyP}: a knowledge graph based literature mining method for drug discovery},
	volume = {19},
	issn = {1471-2105},
	shorttitle = {{SemaTyP}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5975655/},
	doi = {10.1186/s12859-018-2167-5},
	abstract = {Background
Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries.

Methods
Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies’ existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases.

Results
The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs.

Conclusions
In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

Electronic supplementary material
The online version of this article (10.1186/s12859-018-2167-5) contains supplementary material, which is available to authorized users.},
	urldate = {2018-10-10},
	journal = {BMC Bioinformatics},
	author = {Sang, Shengtian and Yang, Zhihao and Wang, Lei and Liu, Xiaoxia and Lin, Hongfei and Wang, Jian},
	month = may,
	year = {2018},
	pmid = {29843590},
	pmcid = {PMC5975655}
}

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