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
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Subjects: | Information Technology (IT) |
| Divisions: | Faculty of Information Technology (FIT) |
| Depositing User: | Thanh Hải Đặng |
| Date Deposited: | 09 Dec 2020 03:20 |
| Last Modified: | 09 Dec 2020 03:20 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4204 |
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