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http://hdl.handle.net/10609/73565
Title: | Estudio comparativo de modelos de machine learning para la detección de dianas microARN |
Author: | Martínez Martínez, Jordi |
Tutor: | Pla Planas, Albert |
Others: | Universitat Oberta de Catalunya Morán Moreno, Jose Antonio Ventura, Carles |
Abstract: | MicroRNAs (miRNA) are small, non-coding RNA molecules of an approximate length of 23 nucleotides, which are involved in post-transcriptional regulation by inhibiting genetic expression by binding to specific target mRNA sites. The present document evaluates the viability of different existing machine learning models for the prediction of miRNA targets. The viability of six different models was examined: k-nearest neighbors, Naive Bayes, artificial neural network, support vector machine, decision tree and random forest. For model training, a database was built with more than 14000 miRNA-mRNA interactions with 19 features, from which 3173 real and 3173 unreal interactions were selected. The random forest model showed the highest efficiency in the prediction of interactions, and was as well one of the most solid and simple to train. Considering the characteristics of models based in random forest and due to their ability to use a great number of features for miRNA target predictions, these models are recommended for future studies of detection and prediction of miRNA targets. |
Keywords: | microRNA machine learning prediction model |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jan-2018 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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Estudio comparativo de modelos de machine learning para la detección de dianas microARN.rar | 4,74 MB | Unknown | View/Open | |
jmartinezrodriguez9TFM0118memoria.pdf | Memoria del TFM | 1,81 MB | Adobe PDF | View/Open |
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