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One of the stakes linked with cardiovascular diseases and other complications is hypertension. Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there is no comprehensive analysis, assessment of diverse features and implementation of various machine-learning (ML) algorithms applied for antihypertensive peptide (AHTP) model construction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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\u20137% in both benchmarking and independent datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The user-friendly online prediction tool, mAHTPred is freely accessible at http:\/\/thegleelab.org\/mAHTPred.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty1047","type":"journal-article","created":{"date-parts":[[2018,12,20]],"date-time":"2018-12-20T12:30:47Z","timestamp":1545309047000},"page":"2757-2765","source":"Crossref","is-referenced-by-count":233,"title":["mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation"],"prefix":"10.1093","volume":"35","author":[{"given":"Balachandran","family":"Manavalan","sequence":"first","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea"}]},{"given":"Shaherin","family":"Basith","sequence":"additional","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea"}]},{"given":"Tae Hwan","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea"},{"name":"Institute of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1444-190X","authenticated-orcid":false,"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"}]},{"given":"Gwang","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea"},{"name":"Institute of Molecular Science and Technology, Ajou University, Suwon, Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2018,12,24]]},"reference":[{"key":"2023062708554486600_bty1047-B1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fninf.2014.00014","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","author":"Abraham","year":"2014","journal-title":"Front. 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