Fuzzy kernel evidence Random Forest for identifying pseudouridine sites

In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.PMID:38622357 | PMC:PMC11018548 | DOI:10.1093/bib/bbae169

Source: Briefings in Bioinformatics - April 15, 2024 Category: Bioinformatics Authors: Mingshuai Chen Mingai Sun Xi Su Prayag Tiwari Yijie Ding Source Type: research