Improved cytokine-receptor interaction prediction by exploiting the negative sample space.

ABSTRACT:

Background

Cytokines act by binding to specific receptors in the plasma membrane of target cells. Knowledge of cytokine-receptor interaction (CRI) is very important for understanding the pathogenesis of various human diseases-notably autoimmune, inflammatory and infectious diseases-and identifying potential therapeutic targets. Recently, machine learning algorithms have been used to predict CRIs. "Gold Standard" negative datasets are still lacking and strong biases in negative datasets can significantly affect the training of learning algorithms and their evaluation. To mitigate the unrepresentativeness and bias inherent in the negative sample selection (non-interacting proteins), we propose a clustering-based approach for representative negative sample selection.

Results

We

SUBMITTER: Nath A 

PROVIDER: S-EPMC7603689 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature