CRF-LSTM Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs

Kaiyin Zhou, Sheng Zhang, Xiangyu Meng, Qi Luo, Yuxing Wang, Ke Ding, Yukun Feng, Mo Chen, Kevin Cohen, Jingbo Xia


Abstract
Sequence labeling of biomedical entities, e.g., side effects or phenotypes, was a long-term task in BioNLP and MedNLP communities. Thanks to effects made among these communities, adverse reaction NER has developed dramatically in recent years. As an illuminative application, to achieve knowledge discovery via the combination of the text mining result and bioinformatics idea shed lights on the pharmacological mechanism research.
Anthology ID:
W18-2321
Volume:
Proceedings of the BioNLP 2018 workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | BioNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
166–171
Language:
URL:
https://aclanthology.org/W18-2321
DOI:
10.18653/v1/W18-2321
Bibkey:
Cite (ACL):
Kaiyin Zhou, Sheng Zhang, Xiangyu Meng, Qi Luo, Yuxing Wang, Ke Ding, Yukun Feng, Mo Chen, Kevin Cohen, and Jingbo Xia. 2018. CRF-LSTM Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs. In Proceedings of the BioNLP 2018 workshop, pages 166–171, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
CRF-LSTM Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs (Zhou et al., 2018)
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PDF:
https://aclanthology.org/W18-2321.pdf