From ade1112c8fb9cd095b487b8007adf345a00939e9 Mon Sep 17 00:00:00 2001 From: coolneng Date: Wed, 7 Jul 2021 14:28:27 +0200 Subject: [PATCH 1/3] Add acknowledgments --- Dissertation.org | 8 ++++---- Dissertation.pdf | Bin 1395330 -> 1398636 bytes assets/abstract.yaml | 2 +- assets/babathesis.latex | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/Dissertation.org b/Dissertation.org index 6988b23..2ca1c66 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -318,9 +318,9 @@ Estas observaciones no son sorprendentes; en la práctica se ha comprobado que e * Diseño y descripción del sistema -La finalidad de este proyecto es el desarrollo de un /pipeline/, con el objetivo de crear un algoritmo de /Deep Learning/ capaz de corregir errores de secuenciación en secuencias de ADN, en particular, en la región CDR3 del TCR. +La finalidad de este proyecto es el desarrollo de un /pipeline/, con el objetivo de crear un algoritmo de /Deep Learning/ capaz de corregir errores de secuenciación en secuencias de ADN, en particular, en la región CDR3 del TCR. Por ende, el trabajo consiste en el desarrollo /end-to-end/ de un sistema de /machine learning/. -El sistema se compone de 2 partes, dado que el algoritmo de /Deep Learning/ no es dependiente del /dataset/ generado /in silico/, y se podría entrenar con cualquier otro conjunto de datos. +El sistema se compone de 2 partes, dado que el algoritmo de /Deep Learning/ es generalizable y se podría proceder al entrenamiento de éste con otro conjunto de datos. #+CAPTION: Descripción del proyecto #+NAME: tab:pipeline @@ -331,8 +331,8 @@ El sistema se compone de 2 partes, dado que el algoritmo de /Deep Learning/ no e El diseño del sistema queda plasmado a continuación: -#+CAPTION: Diseño del sistema. (*A*) Entrenamiento del algoritmo de /Deep Learning/. Como /input/ proporcionamos el número de secuencias, junto con el número de lecturas que deseamos que se simulen. Locigenesis generará 2 archivos en formato FASTQ, que contienen CDR3 con y sin errores de secuenciación, que son el /input/ de locimend para entrenar el modelo de /Deep Learning/, cuya salida es el conjunto de métricas del algoritmo. (*B*) Inferencia del modelo de /Deep Learning/ previamente entrenado y desplegado. Se provee como entrada una secuencia de ADN con errores de secuenciación, el algoritmo procesa ésta y devuelve una secuencia de ADN sin errores -#+ATTR_HTML: :height 50% :width 75% +#+CAPTION: Diseño del sistema. (*A*) Entrenamiento del algoritmo de /Deep Learning/. Como /input/ proporcionamos el número de secuencias, junto con el número de lecturas que deseamos que se simulen. Locigenesis generará 2 archivos en formato FASTQ, que contienen CDR3 con y sin errores de secuenciación, que son el /input/ de locimend, cuya salida es el conjunto de métricas del algoritmo. (*B*) Inferencia del modelo de /Deep Learning/ previamente entrenado y desplegado. 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b/Dissertation.org @@ -350,7 +350,7 @@ El objetivo de este proyecto es generar tanto secuencias de CDR3 con y sin error - CuReSim: simulador de secuenciación que emula la tecnología /Ion Torrent/ cite:Caboche_2014 - Biostrings: manipulación de secuencias biológicas cite:Biostrings -*** Funcionalidades +*** Funcionamiento El programa realiza, parametrizado por 2 parámetros de entrada (número de secuencias diferentes y número de lecturas por el simulador de secuenciación), los siguientes pasos: @@ -366,6 +366,18 @@ locimend es un algoritmo de /Deep Learning/ que corrige errores de secuenciació El objetivo de este proyecto es crear un modelo que pueda inferir la secuencia correcta de ADN, a partir de una secuencia de ADN con errores. Se trata de una reducción de ruido aplicada a un problema de genómica. +La arquitectura del modelo es una /deep feedforward network/, formada por: + +- Capa de entrada +- Capa de /masking/: ignora ciertos valores que se añaden a cada tensor para homogenizar el tamaño del /input/ +- 3 capas densas, seguidas de una capa de /dropout/: el /dropout/ es un mecanismo que disminuye el /overfitting/ o sobreajuste +- Capa densa + +#+CAPTION: Arquitectura de la red neuronal +#+ATTR_HTML: :height 45% :width 70% +#+NAME: fig:locimend +[[./assets/figures/locimend.png]] + La interacción con el modelo se puede realizar mediante: - Interfaz por línea de comandos (CLI) @@ -377,7 +389,7 @@ La interacción con el modelo se puede realizar mediante: - Biopython: manipulación de secuencias biológicas cite:Cock_2009 - FastAPI: /framework/ web para la creación de APIs -*** Funcionalidades +*** Entrenamiento El entrenamiento del modelo se realiza, a partir de 2 archivos FASTQ, en diferentes pasos: @@ -391,6 +403,18 @@ El entrenamiento del modelo se realiza, a partir de 2 archivos FASTQ, en diferen 8. Entrenamiento del algoritmo de /Deep Learning/, con los datos preprocesados 9. Obtención de las métricas que miden el rendimiento del modelo +*** Inferencia + +La inferencia consiste en la predicción de nuevos resultados a partir de un modelo pre-entrenado. Se llevan a cabo los siguientes pasos: + +1. Carga del modelo pre-entrenado +2. Lectura de la secuencia de ADN con errores +3. Codificación basada en el índice de la secuencia de ADN, para obtener une secuencia numérica +4. /One-hot encoding/ de las secuencia numérica, la cual representa la secuencia de ADN +5. Predicción de la secuencia correcta de ADN +6. Descodificación de la secuencia numérica hasta obtener una secuencia de ADN +7. Presentación de la secuencia de ADN correcta inferida por el modelo + ** Reproducibilidad La reproducibilidad de los experimentos en la ciencia es un elemento esencial en el método científico, el cual asegura que una técnica novedosa ofrece resultados verídicos. 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assets/bibliography.bib | 11 +++++++++++ 3 files changed, 22 insertions(+) diff --git a/Dissertation.org b/Dissertation.org index a5f9135..9ee844a 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -444,4 +444,15 @@ En definitiva, locimend adquirió una gran capacidad para discernir entre nucle * Conclusiones * Futuras mejoras + +Los resultados del algoritmo de /Deep Learning/ son satisfactorios aunque presenta ciertas limitaciones. + +En primer lugar, el modelo dado que la red neuronal se entrenó exclusivamente con datos que emulan una secuenciación de Ion Torrent, es probable que las características que contribuyen a las posiciones erróneas sean específicas al proceso de secuenciación de Ion Torrent. Por lo tanto, la inferencia a partir de datos de secuenciación procedentes de otras tecnologías podría ser menos eficaz. Un entrenamiento con varios /dataset/ provenientes de diferentes tecnologías aumentaría la capacidad de generalización del modelo. + +Asimismo, el entrenamiento del modelo se realizó con un conjunto de datos de 20,000 secuencias, un tamaño bastante limitado para una tarea de aprendizaje automático. La generación de /datasets/ de mayor tamaño no era viable debido a limitaciones en las capacidades de cómputo de los recursos disponibles. Por ende, la elaboración de un /dataset/ de mayor envergadura es una tarea pendiente. + +Uno de los factores que más afecta el rendimiento de una red neuronal es el valor de los hiperparámetros. Los hiperparámetros son variables inherentes al modelo, como el optimizador usado, la distribución de los valores iniciales en los pesos del modelo, la tasa de aprendizaje o el /batch size/. Todos estos parámetros son interdependientes cite:book:2164083, por lo que es importante optimizarlos de forma combinatoria. Una optimización más a fondo de los hiperparámetros podría incrementar la eficiencia del modelo. + +Finalmente, el desarrollo y despliegue de un /frontend/ web para la API REST permitiría mejorar la accesibilidad del modelo para los investigadores que deseen usar este modelo. + * Bibliografía diff --git a/Dissertation.pdf b/Dissertation.pdf index b3c5a61b203f3c0a2548a9369190f8a4643418d6..fdf91512a027bb5af439491a618d0fcd0d767fd9 100644 GIT binary patch delta 28133 zcmYhCQ*b6s)U9LNwr$(CZQIEtd1KqQJ+Y06ZQHibckox8o37QW+SM1`yX#qdrBG+^ zuTDS+a0o~UXb4yccnCxYWC&CUbO=lcYzSNkdCBstiEE9r;E{Sx-^EeU+2b`;9NTbMUO$v-6Mj_xs)N z<6&80VR{r>ut$)>+@f&G&ABPv+35F>CPWXg60M!)RsKce{?)=@;E@V)^Gvh&r}&^p zn{lG+$t^xz*Ieqg?M(vD;^+P6@@bOyR8m1@Dq73zKr-8V-X{v})JWc3>N-_xOXd$? z+H5gix;tYqSvuKneSFPd7>C&#Bd(%x6l{-e$1!!3KejXzc-3O3&-Xb+*Lv{k3S}0c zf=$oLsEu>4Nufc0l@XWdt>oS&xz79>CX+8c6iNkNlBYw?r1IIjJ~#B84ze=>1WkYPv1k*SRF ztr2TMTE!Z>?4~6Bf)YRFgRC^x?qUuQFi*Ksios~#yc__bGHsEbH&J9AY{`^4Zpi}W zEMd{0W*HJ?r~ulmRFBdyW zf?=aAf|&?X>2JNsJwSyeSyC_HA+NBayB~iDi^PjZP;`|7lYSdcZ#FMI&uMH%8g_*g z9d@vI+9heg-OWK&=4)f?eV(vcq_doh+#XsefqH)OwZ|m4elSz)(x`t7k{B zVz!+kt=Bpsh8_THs;e^*<^-w;Hi|8rjSXnx5VB4|%{HpvlA|9I6xOz8y3J7yH(CFisM zG)Di>Fh$1edn+NINi5OrD9WIx8sVDQK1h?N?v+27J9|Fyju_}~a9KiTye*wPf{v3%#P z3o15OV3OOOyp?CEb1$BQY#uy*sJSpgDKM_;oCki6@1Xi1R3T?GNQMVAsqD(w^6VG-B11t zN0O(Osq0lfo9^t@S)GKjj;d zIFbvovJ4dqKfpY&poACLlJi}Epki!H#%)Sc<&SyeiMz96s>uu zP@c0#X&(akjCAmqlqAwF`iGGktCP7h&0=#oCTD;IYEv$=Is_y563XCJ{4K7mDi(R- zij^Au zs3-$CcpZuR{i509<2Aux{Q&!>=`z*wie((BhZVWG*N;0s6)SVDHHDU+R#f=U^XZmn*RiO^ksu z@!1r}YtI^`?6=Yw(fNK_02JLfsX1gEewt zNLH{_zE_j-J)-}L3K;HSQ--`1VWGVh&uFjm>cSqls`PqorhS8cu~MZB`A`B)BrDCG+$BcKib^n zg@#~zEHGP~MT@%3i|7QWqde*L+5?Mb2eISIay5R4s5=f~r1bx8Kf*JXwpi%k`-=QG9VV9g( zl~a&+{}h1j_!P+S@F}$@pc~b>2SopF5{VoIfRPGTH#j7Y^z?=WmI44X>wL*odP{l> zVRl2_sEF-FbIKrLgoIGt3nfIcge{?GPz$RTL`i+&(d`hUH@N$hcau%=#y{iB- z7gpfRVOV=3N=r?M;PCYUFJSyiIT7>BA`+#uBuS6;BTLhxiYT>3v@d-jd%}Qw%Uq<* zC1jLnK+_5VKgmRi3T6__v(uvStwNxz8*wecke1pjm z@i1jff=+kS9HEssr0G(K^dhi)mA=gWLG{AgfGP;FmdMr+Swcy2GYNM})g^G|;lU#H z;=O^aOP^t44ZSA*)j^i6)5$eUXg4SRZ+|nX=No?jb{(s)4B#m#Tq40VP%lZz2z#8H z{(fZ`nWm$p2uqf)Na2#xB{j+Qh&dHvV&?N7Iq-)t8#m+`Of*!^ArHn;*~@hT5w(>5~ni?FiN9BMS{*Q_$LZ0B}0yYin#@g z0BV@}MrgfJ0PtFX{im!P-6NxzP;W#eAnt<25D4!Tn`Zi`5VPW>E+5%qCeeCT=9 z5Yem>*sb9kRgLe?!U{OnMxq2-YpD{3`m;JQTNIAAKoS$!4ip!Ie5p<@EwwXt8XB~$4v1r!pW_$*2qE^@QcD1PKcK=_ zlzXQ@W{VZbm(qJY>Ld!M5|_@^%v+e>dN`DGaGr;%4-I7n$^|g`VYfS0xlZtC-ljyXXi0K_e4H zt32dG4d4RS^4B82!*H{{$Zma0$3gTC^3T;HoYEBz2c*J{o@< zN7M)C(^F8!!*ajhk6gbB_Ikw$&Jti&`|B&t0 z?6=-ncwJ;W{yf+`?8<+8JTx0{U|ur!^R`sAkP_b4QS*XlNXz%%^MSb9!tb*KJ7Q}n^{QTRpY&xQ7L?+q+pBGSkGQP=qF zi!#bi`FOCo1degU@*QPKjw91_n`l(%IW>FeAU>gz5ESGeW?ffMiKD~bvGQ8 zCM{ATpCZ6`zAI-djTYe4YYT<7RU?T42+tjJI?;m`Aw3R_1g!xio^Pnc0S)=2KcQ%Zv#hh(%Ic7TO&^JR{Ih}8)}4dM2MUMPF-Eq>(<;!1 zrcK9h+!0ReMGKsFpgS*bQ6cx`WKu3w=HWGK3bu1Q#|oF!h43=67a0#3j#9D#)HaF| z)KZelz~7J@Fa{S;wv`HZn-)ncxV51SwQW}1`{$%LptU}hTjeMvQ`V`ruY z9>WdfD2Pa^Xpvk^B5+*+4M{M^FyXWY3qzyvl^n^YLG`n2BC)gR8}W)jfxc;!JGGj% zUa*L4&sC37fZbW~B!r~Jun98&NYRdph&C-DYY?z%gK0Ax9IpDfDeZ=W%?KO-IPexWf$p4R zxN9*XIw2b*&hulWlan-@sF-6sP~F@Id#jC>`~zJ#I)~ovB<+7Dl^Eh}Pcesg8aK|S zv62kHeNlr=L~mLG)0_uowZtsZJV9WR1fwh-80@DXFi0gKKX3tiW8&Bqdq>19eSN-^ ze5`xd8FZFCT)ADktg>GP_j&3ULM6xu>bqC6_UNi-%|2e9%f5RI@ zs0t)bW}wyjL#XBR$BaR@9p+@|yvAvp-wB?Dd^YL@qAq|=>?@=hu#Ty~Kg{B@t!Fg7 zRbAyoFSO$r>5MWuceWLjz;Wq9-ig& z;D(V2{jj?wb}3rq2z;9+qtH+GA-!Fn$qV?z{7PPZSV z?=i3`TIC4rSUCgp2C8NY1f4Yw?IKR9iZ;8n3q`iKzEDEHd=qd1zem5U{oksvM@)mb zM&q+~V{C#d_LLdqo5p5`jBfMvR>3q@X# zU0YAtq96Yqg8VBpQ1)k$EoguXW&#eBfp#+Ho!>cFb<0j{Osi+j*Aawo`x4p%Ij2dB zOM{cH2B8OV!mtmi{!fC?Lw!O)Sm1`XYY~u-#Ik zSkIk$L^u&X{bAujUJTFa*H)G7QGUvX53B==dI1{bc*Y;TgU13)K9o2@SBjPPJeo+w z#SsDE?h+QPuGoZXlSox3oM2Nr2oEA0+&4D4&1>8&bn-0fBej^muL?(DJ*D_)H9_yt z@$Kua+ql(=te(C2)n2`ZgZ(P8P1oCeb=aVezQb20L}s(eEGTa<(w>h1b#y&6#rlWA?!#{Tth z?rbR@4$}ey;|o zv+9;PYGA%O*9TksWvPaI;<~7#(v>sI5)0J8hua52&W8nO<%NAm#2pTnGUXkvNquoG z50+XqZM=eOK1+4y*hfAhKfp&b%By1T2&W+`# zC2h+$N+8JTl+IDA^^MA=9I`%xyr?iVNx~xWt(}^6${{QiG<9kip~ ziCJKH*vstXr_-k7DntF=d;!bU`|l@n&aRru@5LtBx2?L+m&o}N%pNS+F1oXFV0*6( z;%?P>d|l!_&qW|XF%xY=O(cLg&d4j&?N@#~@tZKSZsuDXbU7DuKRwp_Q01Qu^c+ZM zyoY;;Mmb(OnaO$F5JjDTq^~l~w5B-YujWP-4(>Rkp^E9;O~b!#wp4fRl0z@)GCV_P z;wc-Y)0k3NTa;j?;Ufh-GB0ZdjXZ<|YMVD~#|h9jUDdD+@~+*D{rCXqAqT5lRd;3? 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@@ -1442,3 +1442,14 @@ the Default to Reproducible”}, year = 2013 } + +@book{book:2164083, + title = {Deep Learning with Python}, + author = {François Chollet}, + publisher = {Manning}, + isbn = {9781617294433}, + year = {2017}, + series = {}, + edition = {}, + volume = {}, +}