A novel riboswitch classification based on imbalanced sequences achieved by machine learning

摘要

Machine learning application has been used in many ways in bioinformatics and computational biology. Its use in riboswitch classification is still limited. Existing attempts showed challenges due to imbalanced sequences. Algorithms can classify sequences with majority and minority groups, but they tend to ignore minority group and emphasize on majority class, consequential return a skewed classification. We used a new pipeline including SMOTE for balancing sequences that showed better-classified riboswitch as well as improved performance of algorithms selected. Statistically significant difference observed between balanced and imbalanced in sensitivity, specificity, accuracy and F-score, this proved balanced sequences better for classification of riboswitch. Biological functions and motif search of k-mers in riboswitch families revealed their presence in interior loops, terminal loops and helices. Some of the k-mers were reported to be riboswitch motifs of aptamer domains and critical for metabolite binding. The pipeline can be used in machine learning and deep learning study in other domains of bioinformatics and computational biology suffering from imbalanced sequences. Finally, scientific community can use python source code, the work done and flow to develop packages.