# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 2 Materials and Methods  
### Datasets  
A comprehensive search was undertaken within the U.S. National Library of Medicine's PubMed database, with a specific focus on the retrieval of studies discovering immunogenic peptides. In addition, the built-in \"Similar articles\" tool within PubMed was harnessed to extend the exploration of pertinent literature. It is crucial to note that only human studies were considered throughout the selection process. The structures of peptides were carefully extracted from the respective source articles that reported them. Furthermore, the Immune Epitope Database (IEDB) [27] served as a pivotal reference for established peptide epitopes. This rigorous process resulted in the identification of 212 immunogenic peptides tested in humans, constituting the positive dataset. Additionally, a mirror dataset comprising 212 non-immunogenic peptides was systematically compiled from the literature to constitute the negative set. The criteria for determining immunogenicity included evidence from positive Major Histocompatibility Complex (MHC) binding assays and affirmative T-cell assays conducted in vivo within human subjects. Having accomplished these two conditions, a peptide antigen was considered immunogenic. Conversely, peptides categorized as non-immunogenic lacked evidence of T-cell response despite their bind \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Year** & **Method a** & **Availability Online (as of 1 March 2024)** \\ \hline TTAgP [6] & 2019 & RF & [[https://github.com/bio-coding/TTAgP](https://github.com/bio-coding/TTAgP)]([https://github.com/bio-coding/TTAgP](https://github.com/bio-coding/TTAgP)) \\ iTTCA-Hybrid [7] & 2020 & RF, SVM & [[http://camt.pythonanywhere.com/iTTCA-Hybrid](http://camt.pythonanywhere.com/iTTCA-Hybrid)]([http://camt.pythonanywhere.com/iTTCA-Hybrid](http://camt.pythonanywhere.com/iTTCA-Hybrid)) \\ iTTCA-RF [8] & 2021 & RF & [[http://112.124.26.17:7002/](http://112.124.26.17:7002/)]([http://112.124.26.17:7002/](http://112.124.26.17:7002/)) \\ TAP [11] & 2021 & ML & No \\ PSRTTCA [12] & 2023 & RF & [[http://pmlabstack.pythonanywhere.com/](http://pmlabstack.pythonanywhere.com/)]([http://pmlabstack.pythonanywhere.com/](http://pmlabstack.pythonanywhere.com/)) \\ Vaxijen v2.0 [13] & 2007 & PSL-DA & [[http://www.ddg-pharmfac.net/vaxijen/](http://www.ddg-pharmfac.net/vaxijen/)]([http://www.ddg-pharmfac.net/vaxijen/](http://www.ddg-pharmfac.net/vaxijen/)) \\  & & & Vaxijen/Vaxijen.html \\ LENS [18] & 2023 & Over two dozen separate tools to generate tumor antigen predictions & [[https://gitlab.com/landscape-of-effective-neantigens-software](https://gitlab.com/landscape-of-effective-neantigens-software)]([https://gitlab.com/landscape-of-effective-neantigens-software](https://gitlab.com/landscape-of-effective-neantigens-software)) \\ OpenVax [19] & 2020 & Bioinformatics pipeline & [[https://github.com/openvax](https://github.com/openvax)]([https://github.com/openvax](https://github.com/openvax)) \\ pVACtools [20] & 2020 & Various MHC-I prediction algorithms & [[https://github.com/griffithlab/pVACtools](https://github.com/griffithlab/pVACtools)]([https://github.com/griffithlab/pVACtools](https://github.com/griffithlab/pVACtools)) \\ nextNEOpi [21] & 2022 & WES/WGS/RNA-Seq pipeline & [[https://github.com/icbi-lab/nextNEOpi](https://github.com/icbi-lab/nextNEOpi)]([https://github.com/icbi-lab/nextNEOpi](https://github.com/icbi-lab/nextNEOpi)) \\ TIminer [22] & 2017 & NGS pipeline & [[https://bio.tools/timiner](https://bio.tools/timiner)]([https://bio.tools/timiner](https://bio.tools/timiner)) \\ StackTTCA [26] & 2023 & Stacking ensemble-learning algorithm & No \\ \hline \hline \end{tabular}  
* WES—whole exome sequencing, WGS—whole genome sequencing, RNA-seq—RNA sequencing, NGS—next generation sequencing, RF—random forest, SVM—support vector machine, ML—machine learning, PSL-DA—partial least squares—discriminant analysis.  
\end{table} 
Table 1: List of existing computational methods for tumor T-cell antigen prediction. ing to MHC molecules.