# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 3 Results and Discussion
_ACC137_ represents the cross-covariance between hydrophobicity (E1 descriptor) and the propensity of amino acids for occurrence in \(\alpha\)-helices (E3 descriptor) at \(L=7\), whereas _ACC145_ denotes the cross-covariance between E1 and E4 descriptors at \(L=5\). Finally, the RF and XGBoost models share _ACC254_ as a significant attribute for both. _ACC254_ quantifies the cross-covariance between molecular size and steric properties, along with the propensity of amino acids for occurrence in \(\beta\)-strands (E5 descriptor). The two feature importance techniques utilized aim to identify the most significant features, yet using auto- and cross-covariance feature encoding alone cannot definitively explain why a particular feature holds importance. While it can provide insights into the correlation between specific biological properties encoded with corresponding E-descriptors, it falls short of elucidating causation. Moreover, each identified important feature represents a correlation between different biological properties, lacking consensus among them. Coupled with the generally minimal values regarding feature importance, this limitation prevents us from making conclusive suggestions or drawing conclusions about why one feature might outweigh another in importance. We conducted a comparative analysis of the performance metrics of our three selected models with those of other in silico methods for predicting human tumor antigens. These methods were available online and could be applied without requiring specific programming knowledge. To facilitate this comparison, we evaluated their performance on the current test set and contrasted it with the consensus classification obtained from the majority voting of our three models. Specifically, if two or more models classified a peptide  
Figure 2: Key attributes shared among the three best-performing models: top 10 attributes for each model identified using permutation feature importance technique. The most significant attributes for SVM, RF, and XGBoost algorithms are highlighted in red, green, and blue circles, respectively. The overlapping areas represent joint significant attributes between the algorithms; pink for SVM and XGBoost, grey for RF and XGBoost, and brown for SVM and RF. as immunogenic, it received a consensus classification as an immunogen. The results demonstrated that our three models exhibited superior performance across all assessed statistical measures (Table 5). All in silico tools and methods for predicting human tumor antigens exhibited very poor performance on the external test set utilized in our study. The Matthew's correlation coefficient (MCC) values for all tested models indicated complete disagreement between prediction and observation for TTAgP 1.0, iTTCA-Hybrid, and iTTCA-RF, with predictions no better than random for PSRTTCA. A possible explanation for these dismal results across all assessment metrics is a significant disparity between the datasets used to train these models and the training set employed for our models. Given their remarkable performance, the three selected models have emerged as prime candidates for incorporation into the forthcoming third version of the web-based immunogenicity prediction server, Vaxijen. The newly gathered data spanning 15 years include insights into previously unknown immunogens, which contributed to the updated dataset and models. Consequently, the new dataset along with SVM, RF, and XGBoost models have been selected for integration into the third iteration of the Vaxijen web server (Vaxijen v3.0).