PIPE: a protein–protein interaction passage extraction module for BioCreative challenge-Reference-Cited by-同舟云学术

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PIPE: a protein–protein interaction passage extraction module for BioCreative challenge

Published:2016 Issue: Volume:2016 Page:baw101
ISSN:1758-0463
Container-title:Database
language:en
Short-container-title:Database

Author:

Chang Yung-Chun,Chu Chun-Han,Su Yu-Chen,Chen Chien Chin,Hsu Wen-Lian

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Information Systems

Link

http://academic.oup.com/database/article-pdf/doi/10.1093/database/baw101/17474307/baw101.pdf

Reference39 articles.

1. A comprehensive benchmark of Kernel methods to extract protein–protein interactions from literature;Tikk;PLoS Comput. Biol,2010

2. An approach to improve kernel-based Protein–Protein Interaction extraction by learning from large-scale network data

3. López Y. Nakai K. Patil A. (2015) HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species. Database, 2015,

4. Protein-protein interactions: methods for detection and analysis;Phizicky;Microbiol. Rev,1995

5. Kim S. Islamaj Dogan R. Chatr-Aryamontri A. . (2016) BioCreative V BioC Track Overview: Collaborative Biocurator Assistant Task for BioGRID. Database,

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2. Knowledge-Based Intelligent Text Simplification for Biological Relation Extraction;Informatics;2023-12-11

3. Gradient Boosting over Linguistic-Pattern-Structured Trees for Learning Protein–Protein Interaction in the Biomedical Literature;Applied Sciences;2022-10-11

4. A Protein-Protein Interaction Extraction Approach Based on Large Pre-trained Language Model and Adversarial Training;KSII Transactions on Internet and Information Systems;2022-03-31

5. A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles;Database;2022-01-01

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