Drug repurposing through joint learning on knowledge graphs and literature

Drug repurposing through joint learning on knowledge graphs and literature. Alshahrani, M. & Hoehndorf, R. August, 2018.

Paper doi abstract bibtex

Motivation: Drug repurposing is the problem of ﬁnding new uses for known drugs, and may either involve ﬁnding a new protein target or a new indication for a known mechanism. Several computational methods for drug repurposing exist, and many of these methods rely on combinations of different sources of information, extract hand-crafted features and use a computational model to predict targets or indications for a drug. One of the distinguishing features between different drug repurposing systems is the selection of features. Recently, a set of novel machine learning methods have become available that can efﬁciently learn features from datasets, and these methods can be applied, among others, to text and structured data in knowledge graphs.

@article{alshahrani_drug_2018,
	title = {Drug repurposing through joint learning on knowledge graphs and literature},
	url = {http://biorxiv.org/lookup/doi/10.1101/385617},
	doi = {10.1101/385617},
	abstract = {Motivation: Drug repurposing is the problem of ﬁnding new uses for known drugs, and may either involve ﬁnding a new protein target or a new indication for a known mechanism. Several computational methods for drug repurposing exist, and many of these methods rely on combinations of different sources of information, extract hand-crafted features and use a computational model to predict targets or indications for a drug. One of the distinguishing features between different drug repurposing systems is the selection of features. Recently, a set of novel machine learning methods have become available that can efﬁciently learn features from datasets, and these methods can be applied, among others, to text and structured data in knowledge graphs.},
	language = {en},
	urldate = {2018-11-12},
	author = {Alshahrani, Mona and Hoehndorf, Robert},
	month = aug,
	year = {2018},
}

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