Elena Pianfetti
From sequence to gene expression: a Deep Learning approach to evaluate miRNAs' effect.
Rel. Elisa Ficarra, Marta Lovino. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2021
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Abstract
Proteins perform most of the cell's functions, and each protein must be present in a certain amount to make the cell work correctly. One way to control the number of proteins produced consists of controlling the number of mRNA molecules produced during transcription. The gene sequence includes what will be transcribed to RNA and the information that says how much of a gene product has to be produced. This part of the sequence is called a regulatory sequence. Understanding how this sequence works could lead to better predictions of mRNA expression. In 2020, Agarwal and Shendure developed the Xpresso model in which they predicted the amount of mRNA from the DNA sequence of the promoter region of a protein-coding gene.
Xpresso model is based on a Convolutional Neural Network (CNN) architecture and benefits from additional features associated with the sequence (GC contents and lengths of 5' UTR, Open Reading Frame, and 3' UTR regions, exon junction density, and intron length)
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