4.6 Article

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

期刊

PEERJ
卷 10, 期 -, 页码 -

出版社

PEERJ INC
DOI: 10.7717/peerj.13061

关键词

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

资金

  1. National Center of Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Saudi Arabia
  2. King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [FCC/1/1976-20-01]

向作者/读者索取更多资源

This article introduces a machine learning method that predicts drug targets and indications by combining information from knowledge graphs and published literature. By integrating different types of information, the ranking of targets and indications can be improved.
Biomedical knowledge is represented in structured databases and published in biomed-ical 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据