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Title: | Implementació d'una eina de predicció de dianes de miRNA basat en algorismes de Machine Learning |
Author: | Carrere Molina, Jordi |
Tutor: | Pla Planas, Albert |
Others: | Universitat Oberta de Catalunya Sánchez-Pla, Alex |
Abstract: | miRNA are short non-coding RNA, approximately 22 nucleotides long, with regulatory function of gene expression at post-transcriptional level. About 1500 human miRNA are known, that it is estimated to regulate 30 % of human genes. The identification of miRNA targets is essential to understanding its biological function. Prediction of miRNA targets in silico is a key method to save time and resources to subsequently validate them experimentally. Different softwares can do these predictions applying rule based algorithms. In a recent time, some machine learning algorithms have been applied to model the interaction between miRNA and its target, obtaining great accuracy results. Machine Learning is a method able to recognise patterns in large amounts of data and device a model to classify or predict new data. This thesis presents the tool miRNAforest that models the union between miRNAs and their targets by Random Forest algorithm to classify a new possible target of a given miRNA. miRNAforest is able to predict miRNA targets with an accuracy of 85.78%, 86.93% of sensitivity and 84.66% of specificity. |
Keywords: | microRNA machine learning |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 24-May-2017 |
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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jcarreremTFM0617memoria.pdf | Memòria del TFM | 2,05 MB | Adobe PDF | View/Open |
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