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TOUCAN: a framework for fungal biosynthetic gene cluster discovery.

Author(s): Almeida H, Palys S, Tsang A, Diallo AB

Fungal secondary metabolites (SMs) are an important source of numerous bioactive compounds largely applied in the pharmaceutical industry, as in the production of antibiotics and anticancer medications. The discovery of novel fungal SMs can potentially bene...

Article GUID: 33575642
Machine learning for biomedical literature triage.

Author(s): Almeida H, Meurs MJ, Kosseim L, Butler G, Tsang A

PLoS One. 2014;9(12):e115892 Authors: Almeida H, Meurs MJ, Kosseim L, Butler G, Tsang A

Article GUID: 25551575
mycoCLAP, the database for characterized lignocellulose-active proteins of fungal origin: resource and text mining curation support.

Author(s): Strasser K, McDonnell E, Nyaga C, Wu M, Wu S, Almeida H, Meurs MJ, Kosseim L, Powlowski J, Butler G, Tsang A

Database (Oxford). 2015;2015: Authors: Strasser K, McDonnell E, Nyaga C, Wu M, Wu S, Almeida H, Meurs MJ, Kosseim L, Powlowski J, Butler G, Tsang A

Article GUID: 25754864
Title:Machine learning for biomedical literature triage.
Authors:Almeida H,  Meurs MJ,  Kosseim L,  Butler G,  Tsang A
Link:https://www.ncbi.nlm.nih.gov/pubmed/25551575?dopt=Abstract
DOI:10.1371/journal.pone.0115892
Category:PLoS One
PMID:25551575
Dept Affiliation: GENOMICS
1 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada.
2 Centre for Structural and Functional Genomics, Concordia University, Montreal, QC, Canada.
3 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada; Centre for Structural and Functional Genomics, Concordia University, Montreal, QC, Canada.

Description:

Machine learning for biomedical literature triage.

PLoS One. 2014;9(12):e115892

Authors: Almeida H, Meurs MJ, Kosseim L, Butler G, Tsang A

Abstract

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.

PMID: 25551575 [PubMed - indexed for MEDLINE]