Inferring new relations between medical entities using literature curated term co-occurrences.

ABSTRACT:

Objectives

Identifying new relations between medical entities, such as drugs, diseases, and side effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied. We propose a generic features vector representation that leverages co-occurrences of medical terms, linked with PubMed citations.

Materials and methods

We demonstrate the usefulness of the proposed representation by inferring two types of relations: a drug causes a side effect and a drug treats an indication. To predict these relations

SUBMITTER: Spiro A 

PROVIDER: S-EPMC6951958 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature