swMATH ID: 42978
Software Authors: Manavalan, B.; Basith, S.; Shin, TH; Wei, L.; Lee, G.
Description: mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation. Results: In this study, we utilized six different ML algorithms, namely, Adaboost, extremely randomized tree (ERT), gradient boosting (GB), k-nearest neighbor, random forest (RF) and support vector machine (SVM) using 51 feature descriptors derived from eight different feature encodings for the prediction of AHTPs. While ERT-based trained models performed consistently better than other algorithms regardless of various feature descriptors, we treated them as baseline predictors, whose predicted probability of AHTPs was further used as input features separately for four different ML-algorithms (ERT, GB, RF and SVM) and developed their corresponding meta-predictors using a two-step feature selection protocol. Subsequently, the integration of four meta-predictors through an ensemble learning approach improved the balanced prediction performance and model robustness on the independent dataset. Upon comparison with existing methods, mAHTPred showed superior performance with an overall improvement of approximately 6–7
Homepage: https://academic.oup.com/bioinformatics/article/35/16/2757/5259185
Related Software: Bastion3; hCKSAAP_UbSite; iBitter-SCM; WEKA; UCI-ml
Cited in: 1 Publication

Cited in 1 Serial

1 Soft Computing

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