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Integrative approach for detecting membrane proteins.

Author(s): Alballa M, Butler G

BACKGROUND: Membrane proteins are key gates that control various vital cellular functions. Membrane proteins are often detected using transmembrane topology prediction tools. While transmembrane topology prediction tools can detect integral membrane protein...

Article GUID: 33349234
BENIN: Biologically enhanced network inference.

Author(s): Wonkap SK, Butler G

J Bioinform Comput Biol. 2020 Jun;18(3):2040007 Authors: Wonkap SK, Butler G

Article GUID: 32698722
TooT-T: discrimination of transport proteins from non-transport proteins.

Author(s): Alballa M, Butler G

BMC Bioinformatics. 2020 Apr 23;21(Suppl 3):25 Authors: Alballa M, Butler G

Article GUID: 32321420
TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information.

Author(s): Alballa M, Aplop F, Butler G

PLoS One. 2020;15(1):e0227683 Authors: Alballa M, Aplop F, Butler G

Article GUID: 31935244
Analytical and computational approaches to define the Aspergillus niger secretome.

Author(s): Tsang A, Butler G, Powlowski J, Panisko EA, Baker SE

Fungal Genet Biol. 2009 Mar;46 Suppl 1:S153-S160 Authors: Tsang A, Butler G, Powlowski J, Panisko EA, Baker SE

Article GUID: 19618504
SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models.

Author(s): Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

BMC Bioinformatics. 2014 Jul 01;15:229 Authors: Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

Article GUID: 24980894
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
An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures.

Author(s): Zandi K, Butler G, Kharma N

Front Genet. 2016;7:129 Authors: Zandi K, Butler G, Kharma N

Article GUID: 27499762
Title:TooT-T: discrimination of transport proteins from non-transport proteins.
Authors:Alballa M,  Butler G
Link:https://www.ncbi.nlm.nih.gov/pubmed/32321420?dopt=Abstract
DOI:10.1186/s12859-019-3311-6
Category:BMC Bioinformatics
PMID:32321420
Dept Affiliation: GENOMICS
1 Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada. m_alball@encs.concordia.ca.
2 Department of Computer Science and Software Engineering, Concordia University, Montréal, Québec, Canada.
3 Centre for Structural and Functional Genomics, Concordia University, Montréal, Québec, 24105, Canada.

Description:

TooT-T: discrimination of transport proteins from non-transport proteins.

BMC Bioinformatics. 2020 Apr 23;21(Suppl 3):25

Authors: Alballa M, Butler G

Abstract

BACKGROUND: Membrane transport proteins (transporters) play an essential role in every living cell by transporting hydrophilic molecules across the hydrophobic membranes. While the sequences of many membrane proteins are known, their structure and function is still not well characterized and understood, owing to the immense effort needed to characterize them. Therefore, there is a need for advanced computational techniques takes sequence information alone to distinguish membrane transporter proteins; this can then be used to direct new experiments and give a hint about the function of a protein.

RESULTS: This work proposes an ensemble classifier TooT-T that is trained to optimally combine the predictions from homology annotation transfer and machine-learning methods to determine the final prediction. Experimental results obtained by cross-validation and independent testing show that combining the two approaches is more beneficial than employing only one.

CONCLUSION: The proposed model outperforms all of the state-of-the-art methods that rely on the protein sequence alone, with respect to accuracy and MCC. TooT-T achieved an overall accuracy of 90.07% and 92.22% and an MCC 0.80 and 0.82 with the training and independent datasets, respectively.

PMID: 32321420 [PubMed - in process]