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Prediction of RNA-Protein interactions by an ensemble approach

Grant number: 12/02896-9
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): June 01, 2012
Effective date (End): February 28, 2015
Field of knowledge:Interdisciplinary Subjects
Principal Investigator:Ricardo Zorzetto Nicoliello Vêncio
Grantee:Marcos Abraão de Souza Fonseca
Home Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
Associated research grant:09/09532-0 - Systems biology of the extremophile Halobacterium salinarum: role of the non-coding RNAs in the global gene regulatory network model, AP.JP

Abstract

The gene expression regulation occurs as an essential phenomenon on cellular processes in response to mutual dynamics established between an organism and its environment. In addition to the regulatory elements already known there is growing interests in the regulatory role played by non-coding RNA molecules (ncRNA) that interacting with certain proteins performs their regulatory functions in a post-transcriptional level. Sm family proteins, present in all three domains of life, are key elements in the regulatory network and therefore have been widely studied and characterized.Laboratorial experiments based on immunoprecipitation techniques are able to identify the RNA-protein complex in a satisfactory way, however the time spent in resources and people makes this strategy unfeasible to be applied in a broader context, in relation to organisms and functional elements to be characterized. Aiming to fill this gap, techniques based on machine learning have been one of the alternative approaches to predict protein-RNA interactions. Despite the existence of related works that follows this approach, the molecules specificities that interact have not been achieved in order to ensure a good prediction. In this context, the objective of this work is to combine different models into a single classifier in order to explore the junction of different data perspectives through different biases and search representation.For the methodology evaluation, the strategy will be applied on data from the model organism Halobacterium salinarum and will also be compared with other techniques.