Kaleel, ManazManazKaleelZheng, YandanYandanZhengChen, JialiangJialiangChenFeng, XuanmingXuanmingFengSimpson, Jeremy C.Jeremy C.SimpsonPollastri, GianlucaGianlucaPollastriMooney, CatherineCatherineMooney2021-05-192021-05-192020 the A2020-06Bioinformatics1367-4803http://hdl.handle.net/10197/12182Motivation: The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. Results: Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. Contact: catherine.mooney@ucd.iePrintenThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version Manaz Kaleel, Yandan Zheng, Jialiang Chen, Xuanming Feng, Jeremy C Simpson, Gianluca Pollastri, Catherine Mooney, SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks, Bioinformatics, Volume 36, Issue 11, June 2020, Pages 3343–3349, is available online at: https://doi.org/10.1093/bioinformatics/btaa156.ProteinsComputational biologyAlgorithmsSecretory pathwayMachine learningNeural networksJournal Article36113343334910.1093/bioinformatics/btaa1562021-01-16GOIPG/2014/603https://creativecommons.org/licenses/by-nc-nd/3.0/ie/