From eaef4004ce3fc41b2d9c71970eafae9140564316 Mon Sep 17 00:00:00 2001 From: coolneng Date: Sun, 4 Jul 2021 03:38:37 +0200 Subject: [PATCH] Conclude State of the Art with bioinformatics --- Dissertation.org | 13 +++ Dissertation.pdf | Bin 1204749 -> 1213749 bytes assets/bibliography.bib | 204 +++++++++++++++++++++++++++++++--------- 3 files changed, 171 insertions(+), 46 deletions(-) diff --git a/Dissertation.org b/Dissertation.org index a497ac9..6aeaf55 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -196,6 +196,8 @@ La bioinformática es un campo interdisciplinar en el que intervienen principalm Es tentador atribuir los orígenes de la bioinformática a la reciente convergencia de la secuenciación del ADN, los proyectos genómicos a gran escala, Internet y los superordenadores. Sin embargo, algunos científicos que afirman que la bioinformática se encuentra en su infancia reconocen que los ordenadores eran herramientas importantes en la biología molecular una década antes de que la secuenciación del ADN se convirtiera en algo factible (década de los 1960) cite:Hagen2000. +En los ámbitos de la investigación biológica y médica, la labor del análisis computacional ha aumentado de forma espectacular. La primera ola de esta disciplina se centró en el análisis de secuencias, en la cual aún quedan muchos problemas importantes sin resolver, las necesidades actuales y futuras se centran en la integración sofisticada de conjuntos de datos extremadamente diversos. Estos nuevos tipos de datos proceden de una variedad de técnicas experimentales, muchas de las cuales son capaces de producir datos a nivel de células enteras, órganos, organismos o incluso poblaciones. La principal fuerza impulsora de estos cambios ha sido la llegada de nuevas y eficientes técnicas experimentales, principalmente la secuenciación del ADN, que han conducido a un crecimiento exponencial de las descripciones de las moléculas de proteínas, ADN y ARN cite:book:930. + * Estado del arte Procedemos a realizar un estudio de las metodologías actuales en los ámbitos, introducidos previamente, del /Deep Learning/ y de la bioinformática, con el objetivo de identificar las técnicas que se utilizan a nivel académico y en la industria. @@ -261,6 +263,17 @@ Las capas de convolución (capas C) se utilizan para extraer características y En la actualidad, las CNN se utilizan para /computer vision/, tanto para la clasificación de imágenes cite:howard17_mobil como para la segmentación cite:ronneberger15_u_net, sistemas de recomendación cite:yuan18_simpl_convol_gener_networ_next_item_recom y análisis de sentimientos cite:sadr21_novel_deep_learn_method_textual_sentim_analy. ** Bioinformática + +La expresión ómica engloba una serie de nuevas tecnologías que pueden ayudar a explicar vías, redes y procesos celulares, tanto normales como anormales, mediante el seguimiento simultáneo de miles de componentes moleculares. La ómica abarca un conjunto cada vez más amplio de ramas, desde la *genómica* (el estudio cuantitativo de los genes codificantes de proteínas, los elementos reguladores y las secuencias no codificantes), la *transcriptómica* (ARN y expresión de genes), la *proteómica* (por ejemplo, centrada en la abundancia de proteínas) y la metabolómica (metabolitos y redes metabólicas) hasta los avances en la era de la biología y la medicina postgenómica: farmacogenómica (estudio cuantitativo de cómo la genética afecta a la respuesta del huésped a los fármacos), fisiómica (dinámica y funciones fisiológicas de organismos enteros) cite:Schneider_2011. + +Los métodos de la bioinformática han demostrado ser eficaces para resolver los diversos problemas de la ómica, concretamente para la obtención del estado transcriptómico de una célula (RNA-seq) cite:Peri_2020, reconstrucción de las secuencias de ADN cite:Zerbino_2008, anotación de genomas cite:Spudich_2007 y predicción de la estructura tridimensional de las proteínas cite:Liu_2018. + +El problema de las tasas de error no negligibles en las tecnologías de secuenciación de ADN, de segunda y tercera generación, ha impulsado el desarrollo de múltiples técnicas bioinformáticas para paliar este contratiempo. + +La corrección de errores con respecto a una posición genómica específica puede lograrse disponiendo todas las lecturas de forma horizontal, una tras otra, y examinando la base en cada posición específica de todas estas lecturas. Como los errores son infrecuentes y aleatorios, las lecturas que contienen un error en una posición específica pueden corregirse seleccionando la base más probable inferida a partir de las demás lecturas cite:Yang_2012. Entre estos métodos, destacamos /Coral/ cite:Salmela_2011, /Quake/ cite:Kelley_2010 y /MEC/ cite:Zhao_2017. + +El uso de /Deep Learning/ para la corrección de errores de secuenciación es un área de investigación novedosa, en la cual cabe mencionar /NanoReviser/ cite:10.3389/fgene.2020.00900. + * Objetivos 1. 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