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Machine Learning for Biomedical Literature Triage


Machine Learning for Biomedical Literature Triage

Almeida, Hayda, Meurs, Marie-Jean, Kosseim, Leila, Butler, Greg and Tsang, Adrian (2014) Machine Learning for Biomedical Literature Triage. PLoS ONE .

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Official URL: http://dx.doi.org/10.1371/journal.pone.0115892


This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Concordia University > Research Units > Centre for Structural and Functional Genomics
Item Type:Article
Authors:Almeida, Hayda and Meurs, Marie-Jean and Kosseim, Leila and Butler, Greg and Tsang, Adrian
Journal or Publication:PLoS ONE
Digital Object Identifier (DOI):10.1371/journal.pone.0115892
Keywords:support vector machines, machine learning algorithms, logistic model trees, fungi, database searching, enzyme, machine learning, triage, biocuration, imbalanced dataset
ID Code:979710
Deposited On:24 Feb 2015 17:43
Last Modified:18 Jan 2018 17:49
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