PT  - JOURNAL ARTICLE
AU  - Ma, Wang
AU  - Zhang, Jiawei
AU  - Yao, Hui
TI  - NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation
AID  - 10.26508/lsa.202302255
DP  - 2024 Apr 01
TA  - Life Science Alliance
PG  - e202302255
VI  - 7
IP  - 4
4099  - https://www.life-science-alliance.org/content/7/4/e202302255.short
4100  - https://www.life-science-alliance.org/content/7/4/e202302255.full
SO  - Life Sci. Alliance2024 Apr 01; 7
AB  - Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.