This is a 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
izMiR: computational ab initio microRNA detection
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
ab initio training online
commented on 26 November, 2019
very nice information that you have shared
linux administration course online
commented on 26 November, 2019
nice blog you have shared
ab initio training
commented on 27 November, 2019
nice blog that you have shared
1 comment flagged for moderation
If you posted a comment and think it was mistakenly removed, please contact customer support at [email protected].
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 15 Jul, 2016
Community comments: 3
Metrics
Comments: 3
HTML Views: ...
Learn more about our company.
This is a preprint. It has not completed peer review.
Method Article
izMiR: computational ab initio microRNA detection
Jens Allmer
jLab
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 15 Jul, 2016
Community comments: 3
Metrics
Comments: 3
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
ab initio training online
commented on 26 November, 2019
very nice information that you have shared
linux administration course online
commented on 26 November, 2019
nice blog you have shared
ab initio training
commented on 27 November, 2019
nice blog that you have shared