From 9ec0df0db57cee468fe6928ac9f701a0e88ef3ee Mon Sep 17 00:00:00 2001 From: coolneng Date: Wed, 7 Jul 2021 19:09:53 +0200 Subject: [PATCH] Extend conclusions section --- Dissertation.org | 8 +++++++- Dissertation.pdf | Bin 1482475 -> 1484239 bytes 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/Dissertation.org b/Dissertation.org index 2c81a46..fa5e2c5 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -447,7 +447,7 @@ En definitiva, locimend adquirió una gran capacidad para discernir entre nucle * Conclusiones -El presente estudio plantea el desarrollo de un sistema bioinformático (denominado locimend) de corrección de errores de secuenciación del ADN, basado en /Deep Learning/, y aplicado a secuencias de la región CDR3 del TCR. Locimend fue entrenado con datos generados /in silico/ (en este mismo proyecto) y demuestra un alto rendimiento de predicción y corrección de errores de secuenciación, comparado con las técnicas utilizadas en el estado del arte. El algoritmo opera sobre secuencias de ADN, lo cual permite una fácil integración en el flujo de trabajo de un sistema bioinformático. +El presente estudio plantea el desarrollo de un sistema bioinformático (denominado locimend) de corrección de errores de secuenciación del ADN, basado en /Deep Learning/, y aplicado a secuencias de la región CDR3 del TCR. Locimend fue entrenado con datos generados /in silico/ (en este mismo proyecto) y demuestra un alto rendimiento de predicción y corrección de errores de secuenciación, comparado con las técnicas utilizadas en el estado del arte. El algoritmo opera sobre secuencias de ADN, lo cual permite una fácil integración en el flujo de trabajo de un sistema bioinformático. La elección de una arquitectura de /deep feedforward network/ se debe a la dificultades encontradas con las demás alternativas. Al tratar de usar una CNN con un /input/ unidimensional, no era posible realizar añadir una capa de /MaxPooling/ y /Flatten/, las cuales son indispensables para un buen rendimiento. En el caso de los /autoencoders/, descartamos su utilización debido a los requisitos de cómputo elevados que son necesarios para realizar un entrenamiento efectivo. Finalmente, las /deep feedforward networks/ con su diseño sencillo y su polivalencia han permitido el desarrollo de un algoritmo con una precisión del 89%. Todos los objetivos del proyecto han sido cumplidos con éxito. * Futuras mejoras @@ -461,4 +461,10 @@ Uno de los factores que más afecta el rendimiento de una red neuronal es el val Finalmente, el desarrollo y despliegue de un /frontend/ web para la API REST permitiría mejorar la accesibilidad del modelo para los investigadores que deseen usarlo. +* Disponibilidad del material + +El código fuente de locigenesis se encuentra disponible en [[https://git.coolneng.duckdns.org/coolneng/locigenesis][https://git.coolneng.duckdns.org/coolneng/locigenesis]] + +El código fuente de locimend se encuentra disponible en [[https://git.coolneng.duckdns.org/coolneng/locimend][https://git.coolneng.duckdns.org/coolneng/locimend]] + * Bibliografía diff --git a/Dissertation.pdf b/Dissertation.pdf index a16f9dc0a9563cabd04ce45fc942185e00c051ca..f2f0a32637c63cee5ca4c10d5cdffcdd188c222b 100644 GIT binary patch delta 45267 zcmZsBWmH{FvnB_3cXxMp3vR*P2^!oXz`-TBySqCFhu{+2-QC^8;7+dB?AEW3N(URfI4a(?p7->8bbdT@Paz?vrb 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