From 991191097e6c58f68a41b9a9ebd13fb7059d9c94 Mon Sep 17 00:00:00 2001 From: coolneng Date: Wed, 7 Jul 2021 14:28:27 +0200 Subject: [PATCH] Add acknowledgments --- Dissertation.org | 2 +- Dissertation.pdf | Bin 1395330 -> 1399016 bytes assets/abstract.yaml | 2 +- assets/babathesis.latex | 2 +- 4 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Dissertation.org b/Dissertation.org index 6988b23..1eb9ad5 100644 --- a/Dissertation.org +++ b/Dissertation.org @@ -318,7 +318,7 @@ 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. diff --git a/Dissertation.pdf b/Dissertation.pdf index 41a19590a21a4943417b2814732a2617423bf4a3..15ed76adbf7c9cb7046dd61f5682911acfab9228 100644 GIT binary patch delta 63902 zcmZs?V~;Le)Mi_@?OnEQ+t_8>Ht(`+?y_y$wr$%!PrCag{U-fkBy0YFnRR8XYXzpL zp4F@RHvtpi0=Sr2`1xU+U7Sn}ZDBk%a@0Fxml=_+-y^>S19u~eDZx>yM8jNoME5(_ zWyJb_IoAG#jqY5*tfd@Vo15{*v5byCc`qbQ{Ca6kquFQ(&n-UIc0DGKD&Q#?ZO`V~ zEsiqyEf_lXS1(>OVRyFKW)3!7bn*AzN@Eu$Z^otr0AAi_%$*I#k5Ha%yvhU&oN3(> zsn22eViL#uv42`?7f-bT4gZ?73L-nQZMIRWbIcm)v)ogOz478EbqFZY=T?ir`WIZv*giA zGM&M25JV-(n;csH0&6j481y@M)PVnW0n1#D2S}lim1$}-8ncJTG$at4K^j%{>`1kN z#?f_F&#lf?n8hU5BtYs_!+}~saYibKOElq(-q9Yki7+cp0Eur#I(9+(Xh_rX0AsaF z7HFdgO5#fTS8yy2#n&VQz z0MZN{X6s;eMOQ$_nb$0Pl2C30UqV2{#@~FtIM;Eb)KnZ+TR0 zxeSvW^gdl?dZwri8Vqv_u5PY6oRo|=NJPSmgrOOyXCOxSusQ)7S7&4A;(~$+HECdL zx$;ut(kQKB9W8#tv}gECOdeQb+lh7STE}#OM}*X6v?k>}sG^j{YhiFHPU5R&08I6U zY#)0op*qJYQ@)>}vfG@$=IzFU9zE<;E7TCFa$iw~7ccv2m<))RwlbEIx5Bls>Nv9B zxN^hZcbz&R0KZu<(7(r!xY+ApM*|*6#vu02$+13glPYJv7A>EvTvpmWL_EFp zHC7_)Pcf-uWKL^J0lGsAtBgaF3}M{{(n#c?k1>BdpL5ebjlZ@$P8$cy$c7lF`Hv70 zzfK~U<~=4CS`h(25=ZOgX+yZcJOH-;du4|;I%6-{k**)nKY&?C!&pcILLy;FVT)wy zbmN6P^h%`@z5tG44EKh%D=vXx5JZ}(Q_eHI9%aX~VswcDR`p&)ghEBBwSc=6b%Ng0 z`wgGlIQ^Z}TfmT50;9>*gPV2}vU1k3@Bnp>!}qh;L{1SafP-Mn_|Q=8`mH1XxqvQ| zrM1F?*8Kt5P?cY2k+<2UYPI2SD7fP+nKo+}15~y332i zeeRHd>egcj3fvs(^G@Q-$Kw2f<)aQ|!@rI+H4PtYEdoc^H3Ah#c3^{yBxZAr>nxM$ zEE7rt3}|zYlu}7l^5<-7wnhGpDjaIa^CAANLNQs+n;^FoR#+0&#Ck5u43J}?pO1^) z(kyQo#_}-b#8w1jKOrQNb6IzE@L0aO42N ztz`kfk-K7{HgzM9%t>ylZ_K%@fUKOvLbkhCbjmZyj99q+;oOt5*>i*}PB|@&N{Nkf zYEfKrDKgSD?d5M0x>8O#=}tE1IfLZ2De+hvH7*1O8!&|0pHccYFCUp#I)W6X;zqRw zq_AHlHvf6=`%SUN|2H(EaFn@BhwFT#wPy;l!6!jhDE5iiB6+126KJT`N)ngxaiy1Afdy6hgg~1<0j$@pTnw-mezT-0}EO%wR ztwwx*{b~saH1ii%G;3cdOsgP_m-m_^GB|zKfq8j=yc0(j%q-oQg-)uoAPzm%)fUFC z0Snw(=UfvRT@$SN5zd6u^7^lTmcxAQA*sRm2EykNU^4^NK?7b08i`oKnK_4hsN2w7 zs;UvY3DZURxjc~@FsFtGOuQSc2%Ta$MEPS8Jd=B_sYlVeE17v$za9C!rdwXrpD^%I zu=PPj91|E?$W^PM>7yvzZGg!}PSOuR09Jwlj#WFWbhGmZi>wCzy7ll`kIozzuA>m0 z(d+(xHnM~Mo#Yfg&lVLkVwY{ub{OV2zEYipVO-B3vpyOk>e1H--zvOzLxk@x7>I+{ z$fhQdQscke-Y44!U-eHht5Rbn{uyHD_4Qe}L79a1SS+AYgUP=vcUrs;i&gvr0J_}X z7U}V~yc}~jhdB}zXeeKrk0T%M9ePQStvQfV=W*|9;z4epmk;h+_A;v^VqVLDr)=Ug zcCn^F!L^ivVy$;;I#LhjBKF!DjYLQK2oLd&%ms<58iYu#;$u$ng-amcw0Ga&Iir|~mH#MYa9+4`Vk;^vHhp_|?kEd%UJ$j*b z*nH)1sv-S~&1)}=SZ5hibLuH+p3QaZ14pHg)%dS8%{fr4#=dS|*ttA$cGaj(0eEgH z#>o^FgYuNn>0;-QZ(|5LPe)IV85Q4`wihNO=l*pbk=S37KJ&G?%L_a@18Y#7<~K~j zEoqQ>*qS{W*)Ft7+at`JNqOzvm}}YYhU#@A_+;2=2Mx=@9>`61!-51lFT(c99A8|% zF6?W9(r~x7&==b$sUPLw`Ejw^E`(!&VNQM1aD`hxQ2&F0nAS`ZU_oS%>e#d`R$zS4 zH>ZZQIc{LIFeEnM{{Z<9(EkAY5Agqh_z%ecfcg*U|A6@q*#Ch05BUE;_z%QZBsQd= z3J@-iw3J;WMt~M&q;mO{6Nv?uZj`9f66(9;@`EojY#hMI(W!od*?Rclo{s+=2LKqn zrDxYwiir+P{ifjTGyF!S2x8uL{g&TTi#|-~aOcTpgQSHohrNK7BbQ zk{6Y(E-aiq4UJMT_oTJ#WHjs9YF}u3IdQeSGzde8OW@66waekWGv(8#s8eY9x zBul{R29RWbf2Si!VQKujM`!rWF`D-_Yl{_6mURFX|NF=1nEU5I@nXf~hDghCvZ*cz z&2N@ZVF9|N8wW#r0|bPOww`Wl9OBn%b;}4`Bp~1|<%Oc`?IEi01AMwkdec2Nq2p}5 z!>f~ge1ozrs||XJJ1Q`SUc2<{Z`hbt(9218Kj4oRbVL8q3W29C4I}XEGA)ju(p~4p zl;(>Adv9IneHZ7_m#2J72J5tDJ8!yWANw}0ZrarQ2EvO?UkSs3!k9-jF*~dILMJld z2J0s{m3B=DhI@I_2j$(4?pB>?-Q?qVyNFbU_-cCOPnO))1FbYpk!Y1dyln<@53u&2 zJfIFdDinxwmi4m&JHFX8mV@xL**<(ujI?(d9{<2Ek)!}Dj|I_#3tFZ72!^Myy&3cR zg0pdgj-pU=Xvbp3VXA6;@thG#ihy+EEg%Y?L*pr(RWh}6lrm6Fyj4|_rV--cPal77 zUp4}*MZ0KB|mC2_MVBjqDw2^s5SM6R`qI!;TZHyJfx?L**e_?5=0iaBl#M@_>pvqzlGg<7iQQ#NKl z+Ff^E#K3GY7`3#1t5;MzTVqncA_0<%%BNe<93DXL;KGh>6>+I0;EZ`LwkAJkd!kM@ z7Y((3-Ppb~!>hAkz|5Zb`f&Kh3>Yq_rXMaZ(1>wduX-aTSx(x5*j2*;cDI%RQ{KGV zFb4E2ycq1OFrBq^Q!^-i$cFP#XYF8JFiCe-=jX3Gi_jD+*0ZBLlce9pTrK;bQi|lC zc({vwgJJiW8oSQXUO|Aa2RO@@Xd4SYQ}amWB`iadyPJgbS(?z#Oypey1Nt`pjRS$v zvW-ecb*<kH z@w85a7z$4{jZI(~LX%Kd*wr8if_1DCsoRn)B(Vq^pR<}DO1_f+68s?h#$gu{7fE^^ z_+!at^5(jHVM>^pNb5Vu1wcX=7{Ix$)|u?2HPQw@ob36=D@9^)?@zxbZDV9*?YUJZ z#v>>}Ag;`iE;juZTMWtQHZ>FkVH-FV5l zBBP<4c*K8z1n}6&fA35M|1~5Jz>!1!!!Eg%0UHezdZbXnyi7!MGaD@DOsnGMRiwP@ zlAP|{(Qau8y)X5S)O35-paKfShENB%F?ZH}#U_!EJ4&%)3L5xF6-P*#=5Ez3cLJ9w zo+HK~#ErsNLM-Sr}5~fMXp~w42P}Z%Pd*URrJnXbJgKJQ_X? 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spanish-abstract: "Las nuevas técnicas de secuenciación de ADN (NGS) han revol spanish-keywords: "deep learning, corrección de errores, receptor de linfocitos T, secuenciación de ADN, inmunología" english-abstract: "Next generation sequencing (NGS) techniques 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) found on the surface of lymphocytes, generated in silico and subsequently subjected to a sequencing simulator in order to produce sequencing errors. Using these data, we trained a depp neural network with the aim of generating a computational model that allows the detection and correction of sequencing errors. Our results show that locimend is a model that identifies and corrects DNA sequencing error patterns, obtaining an accuracy of 0,89 and an area under the curve (AUC) of 0,98. The implementation includes a REST API that performs the inference of the correct DNA sequence, from a DNA sequence with errors with the pre-trained model, in order to popularise its use in the scientific community." english-keywords: "deep learning, error correction, DNA sequencing, T-cell receptor, immunology" -acknowdledgments: "" +acknowledgments: "Este proyecto no podría haber sido posible sin el apoyo de numerosas personas. En particular, quiero agradecer especialmente a Carlos Cano Gutiérrez por depositar su voto de confianza al asignarme un proyecto de investigación, el cual no era una propuesta de Trabajo de Fin de Grado. Y a María Soledad Benítez Cantos por su mentorización sido invaluable a lo largo de este trabajo. Su afán por el conocimiento, sus revisiones y comentarios de retroalimentación, su habilidad para exponer un concepto complejo en una frase y su dedicación incondicional al proyecto han sido el pilar central que ha permitido un desenlace favorable de la investigación." diff --git a/assets/babathesis.latex b/assets/babathesis.latex index 2e40dee..9b4c96f 100644 --- a/assets/babathesis.latex +++ b/assets/babathesis.latex @@ -494,7 +494,7 @@ \textbf{Keywords:} $english-keywords$ \end{center} \chapter*{Agradecimientos} - $acknowledgements$ + $acknowledgments$ \tableofcontents \listoftables{} \listoffigures{}