  Background: To investigate the correlation between genomic variation and
certain diseases or phenotypes, the fundamental task is to screen out the
concerning publications from massive literature, which is called literature
triage. Some knowledge bases, including UniProtKB/Swiss-Prot and NHGRI-EBI GWAS
Catalog are created for collecting concerning publications. These publications
are manually curated by experts, which is time-consuming. Moreover, the manual
curation of information from literature is not scalable due to the rapidly
increasing amount of publications. In order to cut down the cost of literature
triage, machine-learning models were adopted to automatically identify
biomedical publications. Methods: Comparing to previous studies utilizing
machine-learning models for literature triage, we adopt a multi-channel
convolutional network to utilize rich textual information and meanwhile bridge
the semantic gaps from different corpora. In addition, knowledge embeddings
learned from UMLS is also used to provide extra medical knowledge beyond
textual features in the process of triage. Results: We demonstrate that our
model outperforms the state-of-the-art models over 5 datasets with the help of
knowledge embedding and multiple channels. Our model improves the accuracy of
biomedical literature triage results. Conclusions: Multiple channels and
knowledge embeddings enhance the performance of the CNN model in the task of
biomedical literature triage. Keywords: Literature Triage; Knowledge Embedding;
Multi-channel Convolutional Network
