# Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model
## 3 Results  
We implemented a pan-cancer classification model using artificial neural networks and gene expression profiles from the GEO database. Since gene expression profiles contain a high number of genes, we used PCA as a feature selection approach that decreased data dimensionality. This method allows us to find linear combinations of the data that capture the most variance in the data. In addition, to remove over-fitting in the unbalanced dataset, we performed an over-sampling technique. To evaluate the performance of the classifier, we used a training and testing data set containing gene expression levels of 16 cancer types. To investigate the consistency of our model, we randomly divided the data 10 times into training and testing data sets. We tested the performance of models using the independent testing data that was not included in the training data. We achieved consistent results, considering their average. We compared the performance of the ANN with a random forest classifier and XGBoost. The performance of the methods in terms of sensitivity and specificity are summarized in Table 3. \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Parameters** \\ \hline \multirow{6}{*}{XGBoost} & Loss = mean squared error \\ \cline{2-2}  & Tree method = gpu hist \\ \cline{2-2}  & Number of estimators = 100 \\ \cline{2-2}  & Learning rate = 0.3 \\ \cline{2-2}  & Gamma = 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{1}{c}{**Dataset**} & \multicolumn{3}{c}{**Sensitivity**} & \multicolumn{3}{c}{**Specificity**} \\ \hline
**Dataset** & **ANN** & **RF** & **XGBoost** & **ANN** & **RF** & **XGBoost** \\ \hline Bladder Urothelial Carcinoma & & & & & & \\ (GSE13507) & 0.85 \(\pm\) 0.09 & 0.0 \(\pm\) 0.0 & **0.97 \(\pm\) 0.03** & 0.58 \(\pm\) 0.22 & **1 \(\pm\) 0.0** & 0.79 \(\pm\) 0.24 \\ \hline Breast invasive carcinoma cancer & & & & & & \\ (GSE2904) & 0.80 \(\pm\) 0.1 & 0.84 \(\pm\) 0.09 & **0.87 \(\pm\) 0.06** & **0.82 \(\pm\) 0.14** & 0.79 \(\pm\) 0.1 & 0.75 \(\pm\) 0.07 \\ \hline Colon adenocarcinoma & & & & & & \\ (GSE41657) & 0.75 \(\pm\) 0.24 & **1 \(\pm\) 0** & 0.97 \(\pm\) 0.06 & **1 \(\pm\) 0** & **1 \(\pm\) 0** & **1 \(\pm\) 0** \\ \hline Esophageal carcinoma & & & & & & \\ (GSE20347) & 0.85 \(\pm\) 0.27 & **0.97 \(\pm\) 0.09** & 0.89 \(\pm\) 0.21 & **1 \(\pm\) 0** & 0.81 \(\pm\) 0.28 & 0.98 \(\pm\) 0.08 \\ \hline Head and Neck squamous cell carcinoma & & & & & & \\ (GSE6631) & 0.69 \(\pm\) 0.27 & **0.96 \(\pm\) 0.08** & 0.93 \(\pm\) 0.08 & **0.92 \(\pm\) 0.09** & 0.84 \(\pm\) 0.14 & 0.92 \(\pm\) 0.11 \\ \hline Kidney Chromophobe & & & & & & \\ (GSE15641) & 0.76 \(\pm\) 0.33 & **0.97 \(\pm\) 0.09** & 0.95 \(\pm\) 0.16 & 0.84 \(\pm\) 0.16 & 0.67 \(\pm\) 0.45 & **0.93 \(\pm\) 0.14** \\ \hline Kidney renal clear cell carcinoma & & & & & & \\ (GSE15641) & 0.97 \(\pm\) 0.05 & **1 \(\pm\) 0** & 0.91 \(\pm\) 0.17 & **1 \(\pm\) 0** & **1 \(\pm\) 0** & **1 \(\pm\) 0** \\ \hline Kidney renal papillary cell carcinoma & & & & & & \\ (GSE15641) & 0.72 \(\pm\) 0.37 & **1 \(\pm\) 0** & **1 \(\pm\) 0** & 0.9 \(\pm\) 0.26 & **1 \(\pm\) 0** & 0.93 \(\pm\) 0.08 \\ \hline Liver hepatocellular carcinoma & & & & & & \\ (GSE45267) & & & & & & \\ \hline Lung squamous cell carcinoma & & & & & & \\ (GSE33479) & & & & & & \\ \hline Lung adenocarcinoma & & & & & & \\ (GSE10072) & & & & & & \\ \hline Prostate adenocarcinoma & & & & & & \\ (GSE6919) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pan-cancer classification performances of the artificial neural network (ANN), XGBoost and random forest (RF). In bold, we highlighted the best results of the models. The accuracy of three classifiers is shown in Figure 1. For most cancers the accuracy of ANN model era above 0.80.