Výsledky vyhledávání - "Lang, Chengkun"

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Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network

Autor: Zhou, Huiwei, Liu, Zhuang, Ning, Shixian, Yang, Yunlong, Lang, Chengkun, Lin, Yingyu, Ma, Kun

Publikováno v: Database-The Journal of Biological Databases and Curation, 2018, 2018: bay071

Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior

Externí odkaz: http://arxiv.org/abs/2001.02107

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Knowledge-aware Attention Network for Protein-Protein Interaction Extraction

Autor: Zhou, Huiwei, Liu1, Zhuang, Ning, Shixian, Lang, Chengkun, Lin, Yingyu, Du, Lei

Publikováno v: Journal of Biomedical Informatics, 2019, 96: 103234

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full

Externí odkaz: http://arxiv.org/abs/2001.02091

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Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge

Autor: Zhou, Huiwei, Ning, Shixian, Yang, Yunlong, Liu, Zhuang, Lang, Chengkun, Lin, Yingyu

Publikováno v: Journal of Biomedical Informatics, 2018, 84:171-178

Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have

Externí odkaz: http://arxiv.org/abs/2001.00295

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Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

Autor: Zhou, Huiwei, Yang, Yunlong, Ning, Shixian, Liu, Zhuang, Lang, Chengkun, Lin, Yingyu, Huang, Degen

Publikováno v: IEEE/ACM TCBB,2018,16(6):1879-1889

Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the developm

Externí odkaz: http://arxiv.org/abs/1912.10604

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Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction

Autor: Zhou, Huiwei, Lang, Chengkun, Liu, Zhuang, Ning, Shixian, Lin, Yingyu, Du, Lei

Publikováno v: BMC Bioinformatics, 2019, 20(1):260

Background: Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomedical experts have built many highly-structured knowledge bases (KBs),

Externí odkaz: http://arxiv.org/abs/1912.10590

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Improving Neural Protein-Protein Interaction Extraction with Knowledge Selection

Autor: Zhou, Huiwei, Li, Xuefei, Yao, Weihong, Liu, Zhuang, Ning, Shixian, Lang, Chengkun, Du, Lei

Publikováno v: Computational Biology and Chemistry, 2019, 83: 107146

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and thei

Externí odkaz: http://arxiv.org/abs/1912.05147

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