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For that, we train, analyze and compare the results of a multilayer perceptron with different hyperparameter choices, such as numbers of hidden layers, activation functions and optimizers, clarifying the advantages and disadvantages of each choice. Our proposed predictor reached a\n                    <jats:italic>F<\/jats:italic>\n                    -score of 0.872, outperforming other state-of-the-art methods for human transposon-derived piRNAs classification. In addition, to better access the generalization of our proposal, we also showed it achieved competitive results when classifying piRNAs of other species.\n                  <\/jats:p>","DOI":"10.1007\/s40747-021-00531-6","type":"journal-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T08:06:20Z","timestamp":1632211580000},"page":"477-487","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Investigating deep feedforward neural networks for classification of transposon-derived piRNAs"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3093-7067","authenticated-orcid":false,"given":"Alisson Hayasi","family":"da Costa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0826-5479","authenticated-orcid":false,"given":"Renato Augusto Corr\u00eaa dos","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2582-1695","authenticated-orcid":false,"given":"Ricardo","family":"Cerri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"1","key":"531_CR1","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1146\/annurev-biochem-060614-034258","volume":"84","author":"YW Iwasaki","year":"2015","unstructured":"Iwasaki YW, Siomi MC, Siomi H (2015) Piwi-interacting RNA: its biogenesis and functions. 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