# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 3 Results and Discussion
_ACC145_ and _ACC147_ denote the cross-covariance between \(E1\) and \(E4\) descriptors at \(L=5\) and \(L=7\), respectively, reflecting the relationship between hydrophobicity, partial specific volume, the number of codons, and the relative frequency of amino acids within defined intervals in protein sequences. _ACC313_ measures the cross-covariance between \(E3\) and \(E1\) descriptors at \(L=3\), indicating the association between amino acid occurrence in \(\alpha\)-helices and hydrophobicity at the specific interval. _ACC234_ represents the cross-covariance between \(E2\) and \(E3\) descriptors at \(L=4\), highlighting the relationship between molecular size, steric properties, and amino acid occurrence in \(\alpha\)-helices within the defined interval. Although there were no shared attributes between the permutation feature importance and drop-column feature importance techniques for the RF method, the drop-column feature importance technique highlighted _ACC537_, _ACC536_, _ACC535_, _ACC534_, _ACC533_, _ACC532_, and _ACC531_ as its most crucial features. This discovery suggests a noteworthy relationship between amino acid propensities for occurrence in \(\alpha\)-helices (descriptor \(E3\)) and \(\beta\)-strands (descriptor \(E5\)) across all possible _lag_-values (1-7). This indicates that these amino acid properties may exert a significant influence on the immunogenicity of tumor peptides. _ACC441_ is the sole attribute shared between the permutation feature importance and drop-column feature importance techniques for the XGBoost model. It quantifies the auto-covariance of E4 descriptors of adjacent amino acids, revealing crucial associations among their partial specific volume, the number of codons, and the relative frequency of amino acids in the protein sequence. \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Top 10 Features** \\ \hline SVM & \\ \multicolumn{2}{l}{Permutation feature importance} & _ACC145_, _ACC137_, _ACC313_, _ACC114_, _ACC117_, _ACC217_, _ACC116_, _ACC147_, _ACC111_, _ACC234_ \\ \multicolumn{2}{l}{Drop-column feature importance} & _ACC313_, _ACC145_, _ACC552_, _ACC446_, _ACC416_, _ACC324_, _ACC247_, _ACC234_, _ACC147_, _ACC141_ \\ \multicolumn{2}{l}{RF} & _ACC511_, _ACC417_, _ACC234_, _ACC117_, _ACC433_, _ACC514_, _ACC247_, _ACC315_, _ACC341_, _ACC254_ \\ \multicolumn{2}{l}{Drop-column feature importance} & _ACC547_, _ACC541_, _ACC537_, _ACC536_, _ACC535_, _ACC534_, _ACC533_, _ACC532_, _ACC531_, _ACC525_ \\ \multicolumn{2}{l}{XGBoost} & _ACC535_, _ACC137_, _ACC516_, _ACC145_, _ACC254_, _ACC151_, _ACC442_, _ACC132_, _ACC531_, _ACC441_ \\ \multicolumn{2}{l}{Drop-column feature importance} & _ACC135_, _ACC511_, _ACC457_, _ACC237_, _ACC455_, _ACC453_, _ACC441_, _ACC432_, _ACC311_, _ACC233_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top 10 attributes ranked according to their importance for SVM, RF, and XGBoost models. The common features between the two feature importance techniques for each of the three models are given in bold. No feature appeared in the top 10 for all the models simultaneously, indicating variability in feature importance across the models. For the drop-column feature importance technique, there were no common features among any of the models. Figure 2 illustrates the common attributes identified through permutation feature importance technique for the three models. The SVM and RF models both consider _ACC117_ and _ACC234_ as the most significant attributes. _ACC117_ signifies the auto-covariance of hydrophobicity (E1 descriptor) at \(L=7\), while _ACC234_ measures the cross-covariance between molecular size and steric properties (E2 descriptor) and the propensity of amino acids for occurrence in \(\alpha\)-helices (E3 descriptor) at \(L=4\). Additionally, the SVM and XGBoost models share two important attributes, _ACC137_ and _ACC145_.