KEMP‐PIP: A Feature‐fusion Based Approach for Pro‐inflammatory Peptide Prediction
Dataset: The dataset used in this study consists of peptide sequences annotated with proinflammatory activity labels. The training set comprises 2872 peptides, each labeled as proinflammatory (positive class) or non‐ proinflammatory (negative class). The test set contains 342 peptide sequences, some of which have corresponding labels for evaluation, while others are unlabeled and used solely for prediction. The dataset was sourced from a publicly available repository https://github.com/ChaoruiYan019/MultiFeatVotPIP
N.B. The paper based on that research is in review in a journal