Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.

ABSTRACT:

Background

Identifying human protein-phenotype relationships has attracted researchers in bioinformatics and biomedical natural language processing due to its importance in uncovering rare and complex diseases. Since experimental validation of protein-phenotype associations is prohibitive, automated tools capable of accurately extracting these associations from the biomedical text are in high demand. However, while the manual annotation of protein-phenotype co-mentions required for training such models is highly resource-consuming, extracting millions of unlabeled co-mentions is straightforward.

Results

In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human pro

SUBMITTER: Pourreza Shahri M 

PROVIDER: S-EPMC8520253 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature