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An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs\u2019 prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.<\/jats:p>","DOI":"10.1093\/bib\/bbab167","type":"journal-article","created":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T11:49:25Z","timestamp":1618055365000},"source":"Crossref","is-referenced-by-count":86,"title":["NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning"],"prefix":"10.1093","volume":"22","author":[{"given":"Md Mehedi","family":"Hasan","sequence":"first","affiliation":[{"name":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan"},{"name":"Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan"}]},{"given":"Md Ashad","family":"Alam","sequence":"additional","affiliation":[{"name":"Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA"}]},{"given":"Watshara","family":"Shoombuatong","sequence":"additional","affiliation":[{"name":"Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand"}]},{"given":"Hong-Wen","family":"Deng","sequence":"additional","affiliation":[{"name":"Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA"}]},{"given":"Balachandran","family":"Manavalan","sequence":"additional","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon 443380, Korea"}]},{"given":"Hiroyuki","family":"Kurata","sequence":"additional","affiliation":[{"name":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan"}]}],"member":"286","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"2021110815061879900_ref1","doi-asserted-by":"publisher","first-page":"6052","DOI":"10.1074\/jbc.RA117.000731","article-title":"Mass spectrometric evidence for neuropeptide-amidating enzymes in Caenorhabditis elegans","volume":"293","author":"Van Bael","year":"2018","journal-title":"J Biol Chem"},{"key":"2021110815061879900_ref2","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1021\/pr020010u","article-title":"Peptidomics-based discovery of novel neuropeptides","volume":"2","author":"Svensson","year":"2003","journal-title":"J Proteome Res"},{"key":"2021110815061879900_ref3","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.npep.2013.10.014","article-title":"Role of neuropeptides in anxiety, stress, and depression: from animals to humans","volume":"47","author":"Kormos","year":"2013","journal-title":"Neuropeptides"},{"key":"2021110815061879900_ref5","doi-asserted-by":"crossref","first-page":"382","DOI":"10.3389\/fnins.2018.00382","article-title":"Biochemical, anatomical, and pharmacological characterization of calcitonin-type neuropeptides in starfish: discovery of an ancient role as muscle relaxants","volume":"12","author":"Cai","year":"2018","journal-title":"Front Neurosci"},{"key":"2021110815061879900_ref6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s10194-017-0726-1","article-title":"Frequent mild head injury promotes trigeminal sensitivity concomitant with microglial proliferation, astrocytosis, and increased neuropeptide levels in the trigeminal pain system","volume":"18","author":"Tyburski","year":"2017","journal-title":"J Headache Pain"},{"key":"2021110815061879900_ref7","doi-asserted-by":"crossref","first-page":"5048616","DOI":"10.1155\/2017\/5048616","article-title":"Neuropeptides and microglial activation in inflammation, pain, and neurodegenerative diseases","volume":"2017","author":"Carniglia","year":"2017","journal-title":"Mediators Inflamm"},{"key":"2021110815061879900_ref8","volume-title":"The Nature of Statistical Learning Theory","author":"Vapnik","year":"2013"},{"key":"2021110815061879900_ref9","doi-asserted-by":"crossref","first-page":"5129","DOI":"10.1038\/s41598-019-41538-x","article-title":"NeuroPIpred: a tool to predict. design and scan insect neuropeptides","volume":"9","author":"Agrawal","year":"2019","journal-title":"Sci Rep"},{"key":"2021110815061879900_ref10","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bby091","article-title":"CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning","author":"Qiang","year":"2018","journal-title":"Brief Bioinform"},{"key":"2021110815061879900_ref11","doi-asserted-by":"crossref","first-page":"573","DOI":"10.3389\/fendo.2018.00573","article-title":"The anti-tumoral properties of orexin\/hypocretin hypothalamic neuropeptides: an unexpected therapeutic role","volume":"9","author":"Couvineau","year":"2018","journal-title":"Front Endocrinol (Lausanne)"},{"key":"2021110815061879900_ref12","article-title":"Genomics- and peptidomics-based discovery of conserved and novel neuropeptides in the American cockroach","author":"Zeng","year":"2020","journal-title":"J Proteome Res"},{"key":"2021110815061879900_ref13","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1002\/jms.742","article-title":"Relative quantitation of peptides in wild-type and Cpe(fat\/fat) mouse pituitary using stable isotopic tags and mass spectrometry","volume":"40","author":"Che","year":"2005","journal-title":"J Mass Spectrom"},{"key":"2021110815061879900_ref14","doi-asserted-by":"crossref","first-page":"146876","DOI":"10.1016\/j.brainres.2020.146876","article-title":"The role of neuropeptides in drug and ethanol abuse: medication targets for drug and alcohol use disorders","volume":"1740","author":"Barson","year":"2020","journal-title":"Brain Res"},{"key":"2021110815061879900_ref15","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1002\/jssc.200700450","article-title":"Peptidomics: the integrated approach of MS, hyphenated techniques and bioinformatics for neuropeptide analysis","volume":"31","author":"Boonen","year":"2008","journal-title":"J Sep Sci"},{"key":"2021110815061879900_ref16","first-page":"42","article-title":"Neurokinin\u2014a polypeptide formed during neuronal activity in man. 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