TCellR2Vec: efficient feature selection for TCR sequences for cancer classification.

ABSTRACT: Cancer remains one of the leading causes of death globally. New immunotherapies that harness the patient's immune system to fight cancer show promise, but their development requires analyzing the diversity of immune cells called T-cells. T-cells have receptors that recognize and bind to cancer cells. Sequencing these T-cell receptors allows to provide insights into their immune response, but extracting useful information is challenging. In this study, we propose a new computational method, TCellR2Vec, to select key features from T-cell receptor sequences for classifying different cancer types. We extracted features like amino acid composition, charge, and diversity measures and combined them with other sequence embedding techniques. For our experiments, we used a dataset of over 50,000 T-c

SUBMITTER: Tayebi Z 

PROVIDER: S-EPMC11622898 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature