From 947ec55633e5e26d4a4fe962083ac1f0697bde3e Mon Sep 17 00:00:00 2001 From: Arity-T Date: Mon, 25 Nov 2024 21:50:08 +0300 Subject: [PATCH] =?UTF-8?q?platinum=20=D0=B2=20=D1=80=D0=B5=D0=B7=D1=83?= =?UTF-8?q?=D0=BB=D1=8C=D1=82=D0=B0=D1=82=D0=B0=D1=85?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- report.tex | 4 +++- results_table/results.xlsx | Bin 11221 -> 11290 bytes 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/report.tex b/report.tex index 1e683cb..9b42ab4 100644 --- a/report.tex +++ b/report.tex @@ -186,12 +186,14 @@ \section{Results} In all works, the construction of machine learning models is essentially a secondary result. First of all, studies show the applicability of these methods to tasks related to the problems of cancer cell resistance to chemotherapy. Also, using machine learning methods, the authors test their hypotheses, confirm or discover links between various characteristics of cancer cells, patient clinical data and drug resistance. - In articles \cite{paclitaxel}, \cite{sers}, \cite{platinum}, \cite{kras}, the authors try to solve the problem of determining drug resistance directly. In \cite{sers}, \cite{platinum}, \cite{kras}, the problem of binary classification (drug resistant vs drug sensitive) is solved, and in \cite{paclitaxel}, cells are classified into 4 classes, which constitute a gradation of the level of resistance of cancer cells to chemotherapy. + In articles \cite{paclitaxel}, \cite{sers}, \cite{platinum}, \cite{kras}, \cite{cervical}, \cite{tabular}, the authors try to solve the problem of determining drug resistance directly. In \cite{sers}, \cite{platinum}, \cite{kras}, \cite{cervical}, \cite{tabular}, the problem of binary classification (drug resistant vs drug sensitive) is solved, and in \cite{paclitaxel}, cells are classified into 4 classes, which constitute a gradation of the level of resistance of cancer cells to chemotherapy. In \cite{paclitaxel}, five different machine learning algorithms were compared, the best results were achieved using support vector machine (accuracy of 93.4\%) and neural network (accuracy of 94.5\%). The classification was based on morphological features and, by constructing effective classifiers, the authors demonstrated that these features are directly related to the level of resistance of cancer cells to chemotherapy. Also, using SHapley Additive exPlanations authors showed that only a 25 of 112 features are really important for the classification. The authors of \cite{sers}, applied robust machine learning algorithm based on principal component analysis and linear discriminant analysis and established an effective predictive model with the accuracy of 96.7\% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100\% for identifying the NPC subjects from healthy ones. Also authors showed the importance of the separation of plasma into upper and lower plasma by comparing model results, e. g. for upper plasma and radiotherapy resistance vs. radiotherapy sensitivity classification task their model achieved 98.7\% accuracy while for lower plasma it is only at level of 93.9\%. + LASSO-based classifier was built by authors of~\cite{platinum}. Their model achieved Area Under Curve (AUC) of 0.864. By analysing their model and its results authors found that three immune-related proteins—CCR1, IGHV3-35, and CD72—along with the presence of postoperative residual tumors, are strong predictors of platinum resistance in EOC patients. + In \cite{kras}, authors firstly applied machine learning algorithms to extract most important features and created seven feature lists, after that they applied four classification algorithms. Their best result was achieved with CATBoost feature list and support vector machine as classification algorithms (accuracy of 93.1\%). Also after analysing recieved feature lists authors were able to identify top genes associated with tumor progression and drug resistance (H2AFZ, CKS1B, TUBA1B, RRM2, BIRC5). \addtocounter{table}{1} diff --git a/results_table/results.xlsx b/results_table/results.xlsx index 260f4d0a7a459d4cc3e03d0be3ba229489bc8836..592a43c6de8af98da4534caf74477ca47b885ef4 100644 GIT binary patch delta 3181 zcmZ8jcQhLc7mpp9+9OtL#ok*eQk1B@cWprI zs1l>ps`B-{?>q0E^W8u0x%d3ez5o1v_jm53eUSrkmX_x7i=v_eZi$v3Z8k5@+;0C> zQ@=f__cS3Ln7nXRlC_5U71;;k%_>)Cv^o(vV?eN)(=&gN@cfL{B`fRfnBkM!S9QVj zaJje2v%h(ne>;6zh!Wj;dLX1`_BtyqpQT$$RS{9Rmy_Gi5*?bfTWLd z=OV&Lo4D`XTw;d!Vlco0JqYwMwk`D;5P33dlA@@R_;gf(FA@tA=yl0(5%I zPG?*w^{rNuWegAp@<%@C*!O6n9DsenF%XD{K|{z`!frd-0)zV#5Nc7agO}*L%-jzNW{R+1mgv8 zhb`?<0(vtyq}fa--i(E0`3uoCfSp#5ws) z&yJ3=eA5NqF5mE8bSCmoNw>WU3A%Hq{P`s~dQOu}+`-feR)4cIi1r^|A}X#m01Iax z%SK0{H39|GQ4W=gS*2SiB||~G+&=x>@_iW(J)?)( zw_5$C^hYj*-u5Tws-sRN^2~Qr6mq#B@cCAOFlxK^B0wu6sogC5!aMQuWym{QwxcDX z4~oJ!`GHt`C)eF#vm|{?IW#Fw&ZyoG`5lEkr^S_0u{UTiC{fbq3bdYR(f|M?Tm(oE zJr^`54|=>yx6Kb)=h)k)8G8Qgi>6Ury@uQEnb&+kFkuICCopKpH(F<0-C!Fm!6Q-CXB0r$SsPhxdV)n{TYhE-5vVv+8Q-XL7 z^meYr)dnPt^<98*epPaEOvfeMc{f^)*iwD!y9mLD)Q#j++uO%Mi~C~}G;k{&2C)Hh zI(aoAozed8%XIdR=qves@o|kiK=teNEG@>X@=sQy(jW+>4ZCGD`u@HBwin6+@)^QP z2My9~>Ge_dck^7dE&%|*<$u%LyoJ9s^5_m^lYci#LE3rFuF93K{W%AyNE zgifPdppliV1WMzJx&Ry(z|HhkiJTkERK4jFz?i$Aqn@GhOT%O zi`dm@d;vM!eo9P*?W+_4PTq0WxULVQRgIc=2}{{@Y0K;JDsM#$_-bDwMekV_G{U;q zJz5cY7*z5s^9@|MQm+Cp)ATuKugcI!As%8j`H2wdYl^Qq&V`*~S~wgXJ;mV2Y5xTL zx7xPNoa-s|1B2REj0><&{dZ%?D?uMLv27K>S+OcP8?O747SD%%ELyCbAuiB#|LDv+ z!;}%D3wl&Lb_kHd?Mk>}A02NwD9`exY@*;^G|Yf&XYmL27&jr3p=x6*<8m@Z z=#nTO$j4ZC$fvH}O(E&DXRIP84)$wBO)(MS#MRjGn+68<8r(80y{)06gDVCbKtH>+ z=wQevd|8xIBF6Cs-FW(8o8g#qoIBOPN1Z>G8A{t`D(zEcSHAaZo=FQ5?*&X$ZuGgk zTh_U?O4a>&bx<2!+|l4Nhcy}(1N?2O#Vol@G;gl_eA0^35L80vd@v?iu=ij3pA$wr zrGY`d3heft+_y>nX1B+oxqXS{9BK{Xsy9nSA*R{f+^}_-Ub@0HY1cDALnK5yj`!!% zjL-h4MHAl@ZANB6{mjs0aPi9MF^fp#Fd?OY;65=bdic5{c>TK^ zkMi*Hc|vy3_!&C+r%g(<&Z{ulZgBID2pE!Ry2);^8IwV-{ILygXqg*cWlcQ`^*xvy z*t}g~Dw>Le*QlM_N_@`%f|e%LJ7Nb!rUSMK^BU)U^mfE9d$-b&Fj=7_#VHGSX+wp& z5xIUmCCSH0>hS8>{jaQTly+>pVp3B>Zyc*ixlgjEtvX#5LUzM=72&vPJYwrsn|MiCBd#8SC&g4Bs|%*>QGb%8(%L4kjp_z4f?`D6zC!29$oOGW6UnIZ?TzZn}r@0V{FgKV8PP1mnwiccRsFt)ymYHX2)jsg))=&=czk1vRe@^OR?cRNH+sh1ELO^dmq2qp zE^OwxnBIq7c&y2xPo4J*I@XI%V=mT6GPcbKliX{C`xPvEq)Bbko&8yzyzJ3Vz3C85 zRl#?2&$8O5L$t=4k3vY!sV2h#xlpO#p~J(kwo~~-M+2MQ$)L<|WQCt5M<(BWGPaQ< z7c<>i_PQ>=2{#(im>j6<@aW>_bTOiObB0Al}+|Ej!jU@0i?KT4nk09gJHM@nf^vWwzsq=aY}#s9+o1F-e!k^lez delta 3100 zcmZuzc{CIX7au0`nlX*#QA3tSVa(V^_9eTS@JPv)Oo#~aWGOYTv6OuoWSE3Ti)HLl zWDQwjj3xV6#%qR0wxq;-z0P;uJKs6q{o~$y&Rzcb-Q}0-GL8)WuJVakFA5~sC`P1M~j4p)r7EAVc2aFnuSkG73N(-5Kx z4jEe+7VIQk?W>LLo^MHsq>}@`K>bs)w}WMi4Oz2zZpPH#j-++_Bp8k`D@W?9W`jT9#qplCFPyJ4m zdPbs(@}lObh?u$Xn#4o5v?mhN`JRJ0{JUS*yEUP<1mT?Q6qJ)p1IwX~m+nZgywyXv z?h0q}w?*lI1WZGejDI5!%pXC|b5EkVpuQHqj-4k%s#c|=xWjFGmKvYp@}f1zo+&=7 z(Bp_xOWe~;d{CAawp8(A>md!3(n=QQRwIvl{A=+s#a~JnVIWwwW+1Cc-h<~AvsYA9 z);=`vA%q%cvtL#1S5=RJ$q+PmI8(V`NF7j^H{9bC)+&W1Wly$G)Hey%j=Z3b9E^Xa z?!e&vE=c;*|M!WgAX={U`_6?oL*M=GiYE%FUQ#w}TQlDZ*buvJbWHlFbY3a=K`x}o z&)BA1SU0b=@FtB_$XLjq7E*Vn<+h}$V?$3VJ#hbs59j)vYO@#G2_eilwEwa9DB{?; zoeYWzudy8G=ABPx;@0OXt<4??4A#ea1~r_9S*vtaaVCSWgYH&dO(AY9ht2H;3yyzk z6`Q{gmaprj)fE<=WDy0ktp|IW>OJ(zoNt|7oNjKmHsGplzYL$m&+uI~by_M<*k(MI$t$=4m8gAb(gq0%t^DNpZ_Sh&WGJ8D?cbtW&%isK#>}42!o_;3dV>Fbtu=Mr`Kk>>pWp?kgoo)T(9ASy%YqZ zT*k&;XuNBDFfw(p?|Wc{_$IUqH*++TPe_&yxzrnWiT;V1O-d48iE!PtsJEj7oo>}f za08o%uX_3eEYs+d#Ib>!k#HCL=mkx>WbJM-!y=e)zv8Oo*MQxU zwx%zNOT@p9%&HmoC|Da2gs@nA?PwN%nglFYW%zMyOa@oTv&ErI^Ke{*Uao;jPlvk{9#_h+Uerv{PI{jG?+9q<4E1TGf< z0C2gr*)xGts?gZWopj(X6YDn4gPLNvcdQeO;gis+sem9O_1f$4W1WnGS5`y;%NQ8e zzNBl5qfxg%B3-(&6n>-JT!y%Dk*nF>^333Sg*l2ie}|%6u^e(|^;sS($td>l)2+UB z2lvAV*v-2U-wx^xk~=s@oFoWWW3?TDU3a=#>&Dx1U6Rfir38!Kktp@e5zP=;e}lwb z9D?s=+i{y0k&GPWK#}v%KUb4E}|$e#YDP<}2w`8ghd}SU%RH#}=W9&5ym`IC;pkRb5Y~%*N`Tz0FD4mAip&#?lB>w{x>0^xy(* z`k@SXw6-!nNTBg8oOaex51Y_&r4V7q=mCcZyG=d@uE3sX+I;w#sUJ5_`MskP5%aQP zaoZF6L3PCp3+YqkDSZHPidAvy-a4HCv}s)wmK*Ny?)=egjGQ52yR|lvb~1h`FFFP2 z{YRGAm|*THDJDUiRemq^{?6Jm!yGT|;t0n=N?#ws?TOS}V~Y1MIwX)V4W87q@3h0o z*@(4WZjfryyyL@U92U~}TH*0ur>aW&jD1>N`ZM3(?fl@&J2&N0yOV`!KCvY5;kai5 z*~3N=n|^BLdoDQT2&Udab9>UfCNs)(B#uq?%EKmqjUY5E*Poy{hrMJe1}_hExnle? zqTXaeQAbD0-}MUXo%L(9J*=1o#H$ENk0IVF(Q2{CnKA9ozs@ zwaR`96PXURr=o+|P|}N;3uI~-juCST(-5o{dhT#iPnwxoF>zPi(CVS`r5&EvVTjIO zZan$;6f>S(m^5dxySCKG95m3;`jQrHN@t5aHXWe)f>nL*jv zmY+U0nI}BbP#Owvh4jy6$rUp}Z0JY-o6*xTz1d2sGKFie2>*JV-fcp79f9bK(qM?Qz;ck%9&8(`6Uvo789Yd>Vbvb{~;hH~3ZcfdZL@_8)J^&cXly