This is a protocol preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Method Article
Jens Allmer
jLab
Corresponding Author
License:
This work is licensed under a CC BY-NC 3.0 License. Read Full License
izMiR is an data analysis workflow that can be used for the detection of pre-miRNAs. The overall system includes two important workflows
1) Training of machine learning classifiers with suitable examples
2) Application of the learned model for detection of new pre-miRNAs.
By using the training workflow it is possible to generate models that can be used on new data to predict whether the given data have potential miRNA hairpins. It is also possible to use prediction workflow directly with the models and input data provided by us. In the latter case compatible features need to be calculated for analysis. These calculations can be done using two webservers. One provided by us (http://www.jlab.iyte.edu.tr/software/izmir) and one by Yones et al. (http://www.fich.unl.edu.ar/sinc/web-demo/mirnafe-full/).
Keywords
MicroRNA detection, computational, ab initio, machine learning
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
none declared
Loading...
Version History
CURRENT STATUS: Posted
Version 1
Posted 15 Jul, 2016
Community comments: 3
Metrics
Comments: 0
HTML Views: ...
Method Article
Jens Allmer
jLab
Corresponding Author
Version History
CURRENT STATUS: Posted
Version 1
Posted 15 Jul, 2016
Community comments: 3
Metrics
Comments: 0
HTML Views: ...
License:
This work is licensed under a CC BY-NC 3.0 License. Read Full License
izMiR is an data analysis workflow that can be used for the detection of pre-miRNAs. The overall system includes two important workflows
1) Training of machine learning classifiers with suitable examples
2) Application of the learned model for detection of new pre-miRNAs.
By using the training workflow it is possible to generate models that can be used on new data to predict whether the given data have potential miRNA hairpins. It is also possible to use prediction workflow directly with the models and input data provided by us. In the latter case compatible features need to be calculated for analysis. These calculations can be done using two webservers. One provided by us (http://www.jlab.iyte.edu.tr/software/izmir) and one by Yones et al. (http://www.fich.unl.edu.ar/sinc/web-demo/mirnafe-full/).
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Loading...