Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications

PeerJ

article

Mona Alshahrani¹  Abdullah Almansour¹  Asma Alkhaldi¹  Maha A. Thafar²  Mahmut Uludag³  Magbubah Essack³  Robert Hoehndorf³ 

[1] National Center for Artificial Intelligence;College of Computers and Information Technology, Taif University;Computer, Electrical and Mathematical Sciences and Engineering Division ,(CEMSE), Computational Bioscience Research Center ,(CBRC), King Abdullah University of Science and Technology ,(KAUST), King Abdullah University of Science and Technology

关键词: Biomedical literature;    Biomedical knowledge graphs;    Drug-target interactions;    Drug-indications;    Multi-modal learning;    Bio-ontologies;    Linked Data;   

DOI  :  10.7717/peerj.13061

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical knowledge and published literature on the prediction of drug targets and drug indications. We demonstrate that our approach using datasets for drug-target interactions and drug indications is scalable to large graphs and can be used to improve the ranking of targets and indications by exploiting features from either structure or unstructured information alone.

【 授权许可】

CC BY