Extremely-randomized-tree-based Prediction of N6-methyladenosine Sites inSaccharomyces cerevisiae

Abstract

Introduction: N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. Methodology: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. Results: Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. Conclusion: In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.

Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus

“…ML algorithms are widely used in molecular biology to systematically elucidate specific molecular markers with associated functions and phenotype data 32 , 33 . Instead of selecting a random classifier for the prediction model development, it is highly recommended to explore multiple classifiers on the same dataset to identify the best classifier 10 , 32 , 34 , 35 . In this regard, we explored support vector machine (SVM), k-neural network (k-NN), random forest (RF), C5.0 decision tree (C5.0), partial least square (PLS), and gradient boosting (GBM).…”

Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites

“…Forth, different ML classifiers are explored to increase prediction performance. It is important not only to integrate different feature encodings [ 111 - 115 ], such as K-nearest neighbors, multivariate information, biochemical properties, and pseudo residues composition, but also to investigate different ML classifiers [ 116 - 122 ], such as an extremely randomized tree, extreme gradient boosting, light gradient boosting, and deep learning. The feature selection technique should remove redundant information to improve performance.…”

T4SE-XGB: interpretable sequence-based prediction of type IV secreted effectors using eXtreme gradient boosting algorithm

“…The purpose of dimensionality reduction or feature selection is to reduce the computational time and complexity of the prediction model, and also to provide more insights into the data abundance (Basith, et al, 2020;Govindaraj, et al, 2020;He, et al, 2018;Jing, et al, 2019;Kang, et al, 2019;Li, et al, 2020;Liu, et al, 2019;Manavalan, et al, 2018;Shi, et al, 2019;Su, et al, 2020;Tang, et al, 2018;Xiong, et al, 2012;Xiong, et al, 2019;. It is indispensable to reduce dimensionality to remove redundant features so that we can reserve the important ones.…”