From 8a329d3582da843e1cabca03702befb2d1a667e2 Mon Sep 17 00:00:00 2001 From: coolneng Date: Mon, 28 Jun 2021 01:02:19 +0200 Subject: [PATCH] Clear page after abstract --- docs/Dissertation.org | 4 ++++ docs/Dissertation.pdf | Bin 109417 -> 110030 bytes 2 files changed, 4 insertions(+) diff --git a/docs/Dissertation.org b/docs/Dissertation.org index 7b5b34a..c625ef5 100644 --- a/docs/Dissertation.org +++ b/docs/Dissertation.org @@ -15,6 +15,8 @@ Las nuevas técnicas de secuenciación de ADN (NGS) han revolucionado la investi *Palabras clave:* deep learning, corrección de errores, receptor de linfocitos T, secuenciación de ADN, inmunología +\clearpage + * Abstract Next generation sequencing (NGS) have revolutionised genomic research. These technologies perform sequencing of millions of fragments of DNA in parallel, which are pieced together using bioinformatics analyses. Although these techniques are commonly applied, they have non-negligible error rates that are detrimental to the analysis of regions with a high degree of polimorphism. In this study we propose a novel computational method, locimend, based on a /Deep Learning/ algorithm for DNA sequencing error correction. It is applied to the analysis of the complementarity determining region 3 (CDR3) of the T-cell receptor (TCR), generated in silico and subsequently subjected to a sequencing simulator in order to produce sequencing errors. Using these data, we trained a convolutional neural network (CNN) with the aim of generating a computational model that allows the detection and correction of sequencing errors. @@ -22,6 +24,8 @@ Next generation sequencing (NGS) have revolutionised genomic research. 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