Document triage for identifying protein–protein interactions affected by mutations: a neural network ensemble approach-Reference-Cited by-同舟云学术

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Document triage for identifying protein–protein interactions affected by mutations: a neural network ensemble approach

Published:2018-01-01 Issue: Volume:2018 Page:
ISSN:1758-0463
Container-title:Database
language:en
Short-container-title:

Author:

Luo Ling¹,Yang Zhihao¹,Lin Hongfei¹,Wang Jian¹

Affiliation:

1. College of Computer Science and Technology, Dalian University of Technology, Dalian, China

Funder

National Key Research and Development Program of China

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Trans-Century Training Program Foundation

Ministry of Education of China

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/bay097/27329429/bay097.pdf

Reference35 articles.

1. Text mining genotype-phenotype relationships from biomedical literature for database curation and precision medicine;Singhal;PLoS Comput. Biol.,2016

2. The BioGRID interaction database: 2017 update;Chatr-Aryamontri;Nucleic Acids Res.,2017

3. BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID;Kim;Database (Oxford),2016

4. The protein–protein interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text;Krallinger;BMC Bioinformatics,2011

5. tmVar: a text mining approach for extracting sequence variants in biomedical literature;Wei;Bioinformatics,2013

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1. One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning;Entropy;2021-09-28

2. Extracting Protein-Protein Interactions Affected by Mutations via Auxiliary Task and Domain Pre-trained Model;2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2020-12-16

3. Using deep learning to identify translational research in genomic medicine beyond bench to bedside;Database;2019-01-01

4. Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine;Database;2019-01-01

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