eprintid: 4204
rev_number: 6
eprint_status: archive
userid: 327
dir: disk0/00/00/42/04
datestamp: 2020-12-09 03:20:02
lastmod: 2020-12-09 03:20:02
status_changed: 2020-12-09 03:20:02
type: article
metadata_visibility: show
creators_name: Pham, Thi Quynh Trang
creators_name: Bui, Manh Thang
creators_name: Dang, Thanh Hai
creators_id: trangptq@vnu.edu.vn
creators_id: hai.dang@vnu.edu.vn
title: Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: Chemical compounds (drugs) and diseases are among top searched keywords on the PubMed database of biomedical literature by biomedical researchers all over the world (according to a study in 2009). Working with PubMed is essential for researchers to get insights into drugs’ side effects (chemical-induced disease relations (CDR), which is essential for drug safety and toxicity. It is, however, a catastrophic burden for them as PubMed is a huge database of unstructured texts, growing steadily very fast (~28 millions scientific articles currently, approximately two deposited per minute). As a result, biomedical text mining has been empirically demonstrated its great implications in biomedical research communities. Biomedical text has its own distinct challenging properties, attracting much attetion from natural language processing communities. A large-scale study recently in 2018 showed that incorporating information into indenpendent multiple-input layers outperforms concatenating them into a single input layer (for biLSTM), producing better performance when compared to state-of-the-art CDR classifying models. This paper demonstrates that for a CNN it is vice-versa, in which concatenation is better for CDR classification. To this end, we develop a CNN based model with multiple input concatenated for CDR classification. Experimental results on the benchmark dataset demonstrate its outperformance over other recent state-of-the-art CDR classification models.
date: 2020-05
date_type: published
publisher: VNU
full_text_status: none
publication: VNU Journal of Science: Computer Science and Communication Engineering
volume: 36
number: 1
pagerange: 11-16
refereed: TRUE
issn: 2588-1086
related_url_url: https://jcsce.vnu.edu.vn/index.php/jcsce/article/view/237
citation:   Pham, Thi Quynh Trang and Bui, Manh Thang and Dang, Thanh Hai  (2020) Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature.  VNU Journal of Science: Computer Science and Communication Engineering, 36  (1).   pp. 11-16.  ISSN 2588-1086