Paper Info
Title
Protein-Protein Interaction Article Classification Using a Convolutional Recurrent Neural Network with Pre-trained Word Embeddings.
Abstract
Curation of protein interactions from scientific articles is an important task, since interaction networks are essential for the understanding of biological processes associated with disease or pharmacological action for example. However, the increase in the number of publications that potentially contain relevant information turns this into a very challenging and expensive task. In this work we used a convolutional recurrent neural network for identifying relevant articles for extracting information regarding protein interactions. Using the BioCreative III Article Classification Task dataset, we achieved an area under the precision-recall curve of 0.715 and a Matthew's correlation coefficient of 0.600, which represents an improvement over previous works.
Year
DOI
Venue
2017
10.1515/jib-2017-0055
JOURNAL OF INTEGRATIVE BIOINFORMATICS
Keywords
Field
DocType
Literature retrieval,protein-protein interactions,machine learning,recurrent neural networks,word embeddings
Data mining,Protein–protein interaction,Computer science,Recurrent neural network,Artificial intelligence,Semantics
Journal
Volume
Issue
ISSN
14
SP4
1613-4516
Citations 
PageRank 
References 
0
0.34
16
Authors
2
Name
Order
Citations
PageRank
Sérgio Matos141529.51
Antunes, R.222.40