Prediction of enzyme function using an interpretable optimized ensemble learning framework.

ABSTRACT: Accurate prediction of enzyme function, particularly for newly discovered uncharacterized sequences, is immensely important for modern biological research. Recently, machine learning (ML) based methods have shown promise. However, such tools often suffer from complexity in feature extraction, interpretability, and generalization ability. In this study, we construct a dataset for enzyme functions and present an interpretable ML method, SOLVE (Soft-Voting Optimized Learning for Versatile Enzymes), that addresses these issues by using only combinations of tokenized subsequences from the protein's primary sequence for classification. SOLVE utilizes an ensemble learning framework integrating random forest (RF), light gradient boosting machine (LightGBM) and decision tree (DT) models with an opt

SUBMITTER: Dhibar S 

PROVIDER: S-EPMC12424445 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature