Extracting microRNA-gene relations from biomedical literature using distant supervision.

ABSTRACT: Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occu

SUBMITTER: Lamurias A 

PROVIDER: S-EPMC5338769 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature