StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens.

ABSTRACT:

Background

The identification of tumor T cell antigens (TTCAs) is crucial for providing insights into their functional mechanisms and utilizing their potential in anticancer vaccines development. In this context, TTCAs are highly promising. Meanwhile, experimental technologies for discovering and characterizing new TTCAs are expensive and time-consuming. Although many machine learning (ML)-based models have been proposed for identifying new TTCAs, there is still a need to develop a robust model that can achieve higher rates of accuracy and precision.

Results

In this study, we propose a new stacking ensemble learning-based framework, termed StackTTCA, for accurate and large-scale identification of TTCAs. Firstly, we constructed 156 different baseline models by using 12 differ

SUBMITTER: Charoenkwan P 

PROVIDER: S-EPMC10386778 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature