# Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model
## 4 Discussion  
In this study, we used the gene expression profiles of 749 cancer patients and 485 normal samples of 16 different cancer types from GEO database to provide a pan-cancer analysis using a deep learning approach compared with random forest classifier and XGBoost models.  
Previous studies have already shown a higher performance of ANNs compared with logistic regression in cancer [36; 37].  
However, in pan-cancer analysis, there is no clear evidence of the predictive accuracy of ANNs in cancer diagnosis (tumor vs. normal prediction). There are few studies that have demonstrated the abilities of ANNs to predict cancer samples, and the applications to date have been mainly performed to few cancer types [38; 39]. In addition, we applied a novel recent algorithm, XGBoost, which obtained good classifier performance in cancer in other studies [17; 18]. Many studies have demonstrated the role of XGBoost in the prediction of origin of tissues in cancer, and few studies in tumor vs. normal prediction [17; 18].  
In our study, the performance of the ANN model is compared with a random forest classifier and XGBoost to carry out a comparative analysis of classification models. The classification was performed on gene expression levels of cancer and normal tissues, focusing on two main aspects in the ANN model: first, we used PCA as method to reduce data dimensionality; second, we propose the use of the Adam optimization algorithm.  
The ANN model achieved accuracies greater than 0.80 in most cancers based on key components derived from PCA. Random forest achieved high performances, close to the ANN model. We conclude that the ANN model and random forest have similar high performances. Random forest obtained a slightly better performance than the ANN, but it was not significant (\(t\)-test: \(p\)-value \(>\) 0.5). XGBoost achieved a better performance in accuracy (\(p\)-value = 0.001), sensitivity (\(p\)-value = 0.001) and AUC (\(p\)-value = 0.049) compared with the ANN. No statistically significant difference was revealed between the ANN and random forest in sensitivity, specificity and accuracy.  
These findings are consistent with reports presented in other studies. Indeed, recent studies have suggested neural networks as promising tools in classification analysis using gene expression data [3]. [PERSON] et al. demonstrated that random forest and deep learning approaches obtained high and similar performance using cancer sequencing data [40].  
In summary, our results demonstrated that our ANN classifier obtained classification performance similar to random forest using a limited number of samples.  
The models are only tested on one dataset for each cancer type. This study found high and similar performance of the three models, but further studies should be performed on other datasets in future works, and the associations between gene expression levels and genetic aberrations should be also investigated.  
Despite the interesting results of our study, there are some limitations to be noted. First, the ANN is difficult to configure, as the model is dependent on the structure of the network used, the choice of activation functions, the regularization approach used, the depth of the network and other factors still. Second, the ANN model should be further applied to larger samples, as several studies reported that the performance of a classifier based on an ANN increases with the number of samples [41]. Another limitation of the current approach is that we modelled and predicted the model considering a training and testing dataset but not a validation set. This is due to small size of the pan-cancer dataset, which limits its usage only to testing evaluation. The lack of a validation set could induce an overfitting bias that needs to be explored in a future work. The authors will expand their research in order to increase the models' accuracy using other validation sets and multi-omics data.