From 6270023c876a960bdc8c3ec55110eaf56224b88a Mon Sep 17 00:00:00 2001 From: coolneng Date: Wed, 7 Jul 2021 16:52:10 +0200 Subject: [PATCH] Add future improvements --- Dissertation.org | 11 +++++++++++ Dissertation.pdf | Bin 1478870 -> 1480898 bytes 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 = {}, +}