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Effectidor: an automated machine-learning based web server for the prediction of type-III secretion system effectors
Year:
2022
Source of publication :
bioinformatics (source)
Authors :
Volume :
Co-Authors:

Naama Wagner,

Oren Avram,

Dafna Gold-Binshtok,

Ben Zerah,

Doron Teper

Tal Pupko

 

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

Motivation

Type-III secretion systems are utilized by many Gram-negative bacteria to inject type-3 effectors (T3Es) to eukaryotic cells. These effectors manipulate host processes for the benefit of the bacteria and thus promote disease. They can also function as host-specificity determinants through their recognition as avirulence proteins that elicit immune response. Identifying the full effector repertoire within a set of bacterial genomes is of great importance to develop appropriate treatments against the associated pathogens.

Results

We present Effectidor, a user-friendly web server that harnesses several machine-learning techniques to predict T3Es within bacterial genomes. We compared the performance of Effectidor to other available tools for the same task on three pathogenic bacteria. Effectidor outperformed these tools in terms of classification accuracy (area under the precision–recall curve above 0.98 in all cases).

Note:
Related Files :
automated machine-learning
Type-III secretion
Show More
Related Content
More details
DOI :
10.1093/bioinformatics/btac087
Article number:
0
Affiliations:
Database:
Google Scholar
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
58478
Last updated date:
06/04/2022 17:01
Creation date:
06/04/2022 17:00
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Scientific Publication
Effectidor: an automated machine-learning based web server for the prediction of type-III secretion system effectors

Naama Wagner,

Oren Avram,

Dafna Gold-Binshtok,

Ben Zerah,

Doron Teper

Tal Pupko

 

Effectidor: an automated machine-learning based web server for the prediction of type-III secretion system effectors

Motivation

Type-III secretion systems are utilized by many Gram-negative bacteria to inject type-3 effectors (T3Es) to eukaryotic cells. These effectors manipulate host processes for the benefit of the bacteria and thus promote disease. They can also function as host-specificity determinants through their recognition as avirulence proteins that elicit immune response. Identifying the full effector repertoire within a set of bacterial genomes is of great importance to develop appropriate treatments against the associated pathogens.

Results

We present Effectidor, a user-friendly web server that harnesses several machine-learning techniques to predict T3Es within bacterial genomes. We compared the performance of Effectidor to other available tools for the same task on three pathogenic bacteria. Effectidor outperformed these tools in terms of classification accuracy (area under the precision–recall curve above 0.98 in all cases).

Scientific Publication
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