# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 1 Introduction
### Computational Methods for Tumor T-Cell Antigen Prediction
#### 1.2.1 Machine Learning Prediction Tools  
TTAgP stands as a precise bioinformatic tool utilizing the random forest algorithm to predict antitumor peptides, presented in the context of MHC Class-I [6]. It is trained and validated based on several features of 922 peptides derived from the TANTIGEN dataset ([[http://cvc.dfci.harvard.edu/tadb/](http://cvc.dfci.harvard.edu/tadb/)]([http://cvc.dfci.harvard.edu/tadb/](http://cvc.dfci.harvard.edu/tadb/)) (accessed on 16 February 2024)). The performance of this method was evaluated against other human tumor antigen prediction programs, demonstrating superior results. iTTCA-Hybrid stands as an automated, sequence-based predictor, meticulously designed for the identification of tumor MHC Class-I T cell antigens [7]. This predictive framework employs two machine learning models, Random Forest (RF), and Support Vector Machine (SVM) [8]. Leveraging a sophisticated approach, iTTCA-Hybrid integrates five distinct feature encoding strategies, encompassing amino acid composition, dipeptide composition, pseudo-amino acid composition, distribution of amino acid properties in sequences, and physicochemical properties derived from the AAiindex [9]. Additionally, an oversampling approach, specifically the Synthetic Minority Over-sampling Technique (SMOTE), is incorporated to enhance the model's predictive performance [10]. TAP 1.0 is an immunoinformatic tool specifically tailored for the prediction of tumor T cell antigens [11]. In this work, 544 descriptors of chemical-physical properties extracted from the AAiindex database [9] were calculated for a total of 1184 tumor and non-tumor antigens. Its development involved a meticulous assessment of 15 machine learning algorithms applied to the classification of tumor antigens. Notably, the Quadratic Discriminant Analysis (QDA) model demonstrated optimal equilibrium across various performance measures, positioning itself as the most adept choice within this evaluative framework. PSRTTCA is a recently developed algorithm aimed at enhancing the identification and characterization of tumor T-cell antigens [12]. This novel approach utilizes propensity score representation learning, building on the Scoring Card Method (SCM), which generates diverse sets of propensity scores for amino acids, dipeptides, and g-gap dipeptides extracted from sequences associated with tumor T-cell antigens. They are later fed to an RF meta-classifier and parameter optimization is performed. After that, the best feature vectors are selected based on their statistical performance and a final predictive model is constructed. Furthermore, PSRTTCA offers the flexibility to extend its application to classify various other protein and peptide functions based solely on their primary sequences.  
Vaxijen stands as the pioneering server for the alignment-independent prediction of protective antigens [13]. It employs distinct positive and negative sets, each comprising 100 well-established antigens and 100 known non-antigens, respectively. Adopting a machine learning (ML) approach, the method represents each protein within the set as a string of z-scales [14], encapsulating the principal physicochemical properties of constituent amino acid residues. These representations are subsequently transformed into uniform vectors through auto-cross covariance (_ACC_) [15]. The sets of vectors undergo a systematic analysis facilitated by the genetic algorithm (GA) [16], which serves to discern pertinent variables. Subsequently, partial least squares-based discriminant analysis (PLS-DA) [17] is applied to construct the final prediction model.