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In this study, we have developed a machine learning framework called iAMY-RECMFF to discriminate amyloidgenic from non-amyloidgenic peptides. In our model, we first encoded the peptide sequences using the residue pairwise energy content matrix. We then utilized Pearson\u2019s correlation coefficient and distance correlation to extract useful information from this matrix. Additionally, we employed an improved similarity network fusion algorithm to integrate features from different perspectives. The Fisher approach was adopted to select the optimal feature subset. Finally, the selected features were inputted into a support vector machine for identifying amyloidgenic peptides. Experimental results demonstrate that our proposed method significantly improves the identification of amyloidgenic peptides compared to existing predictors. This suggests that our method may serve as a powerful tool in identifying amyloidgenic peptides. To facilitate academic use, the dataset and codes used in the current study are accessible at https:\/\/figshare.com\/articles\/online_resource\/iAMY-RECMFF\/22816916 . <\/jats:p>","DOI":"10.1142\/s0219720023500233","type":"journal-article","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T02:58:01Z","timestamp":1696042681000},"source":"Crossref","is-referenced-by-count":2,"title":["iAMY-RECMFF: Identifying amyloidgenic peptides by using residue pairwise energy content matrix and features fusion algorithm"],"prefix":"10.1142","volume":"21","author":[{"given":"Zizheng","family":"Yu","sequence":"first","affiliation":[{"name":"School of Communications and Electronics Jiangxi, Science and Technology Normal University, Nanchang 330013, P. R. 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