{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T19:57:48Z","timestamp":1784750268504,"version":"3.55.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,10,11]],"date-time":"2019-10-11T00:00:00Z","timestamp":1570752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61863010"],"award-info":[{"award-number":["61863010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Research and Development Program of Shandong Province of China","award":["2019GGX101001"],"award-info":[{"award-number":["2019GGX101001"]}]},{"name":"Natural Science Foundation of Shandong Province of China","award":["ZR2017MA014"],"award-info":[{"award-number":["ZR2017MA014"]}]},{"name":"Natural Science Foundation of Shandong Province of China","award":["ZR2018MC007"],"award-info":[{"award-number":["ZR2018MC007"]}]},{"DOI":"10.13039\/501100015642","name":"Project of Shandong Province Higher Educational Science and Technology Program","doi-asserted-by":"crossref","award":["J17KA159"],"award-info":[{"award-number":["J17KA159"]}],"id":[{"id":"10.13039\/501100015642","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Scientific Research Fund of Hunan Provincial Key Laboratory of Mathematical Modelling and Analysis in Engineering","award":["2018MMAEZD10"],"award-info":[{"award-number":["2018MMAEZD10"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ACI-1548562"],"award-info":[{"award-number":["ACI-1548562"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g-gap dipeptide composition (g-gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8\u201312.5% and 3.8\u20139.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source codes and data are publicly available at https:\/\/github.com\/QUST-AIBBDRC\/SubMito-XGBoost\/.<\/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\/btz734","type":"journal-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T11:28:48Z","timestamp":1569497328000},"page":"1074-1081","source":"Crossref","is-referenced-by-count":156,"title":["SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting"],"prefix":"10.1093","volume":"36","author":[{"given":"Bin","family":"Yu","sequence":"first","affiliation":[{"name":"College of Mathematics and Physics, Qingdao University of Science and Technology , Qingdao 266061, China"},{"name":"School of Life Sciences, University of Science and Technology of China , Hefei 230027, China"},{"name":"Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology , Qingdao 266061, China"},{"name":"School of Mathematics and Statistics, Changsha University of Science and Technology , Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenying","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Qingdao University of Science and Technology , Qingdao 266061, China"},{"name":"Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology , Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Qingdao University of Science and Technology , Qingdao 266061, China"},{"name":"Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology , Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjun","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"},{"name":"School of Aerospace Engineering, Xiamen University , Xiamen 361001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Qingdao University of Science and Technology , Qingdao 266061, China"},{"name":"Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology , Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,10,11]]},"reference":[{"key":"2023020108352089400_btz734-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00232-015-9868-8","article-title":"Prediction of protein submitochondrial locations by incorporating dipeptide composition into chou\u2019s general pseudo amino acid composition","volume":"249","author":"Ahmad","year":"2016","journal-title":"J. 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Biol"},{"key":"2023020108352089400_btz734-B26","first-page":"129","author":"Kira","year":"1992"},{"key":"2023020108352089400_btz734-B28","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.patcog.2017.02.025","article-title":"Granular multi-label feature selection based on mutual information","volume":"67","author":"Li","year":"2017","journal-title":"Pattern Recogn"},{"key":"2023020108352089400_btz734-B29","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1039\/C4MB00340C","article-title":"Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction","volume":"11","author":"Li","year":"2015","journal-title":"Mol. Biosyst"},{"key":"2023020108352089400_btz734-B30","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.neucom.2013.08.004","article-title":"LibD3C: ensemble classifiers with a clustering and dynamic selection strategy","volume":"123","author":"Lin","year":"2014","journal-title":"Neurocomputing"},{"key":"2023020108352089400_btz734-B31","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s10441-013-9181-9","article-title":"Using over-represented tetrapeptides to predict protein submitochondria locations","volume":"61","author":"Lin","year":"2013","journal-title":"Acta Biotheor"},{"key":"2023020108352089400_btz734-B32","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.jtbi.2011.10.015","article-title":"Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization","volume":"293","author":"Mei","year":"2012","journal-title":"J. Theor. 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Biol"},{"key":"2023020108352089400_btz734-B36","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jtbi.2005.08.016","article-title":"Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition","volume":"240","author":"Shen","year":"2006","journal-title":"J. Theor. Biol"},{"key":"2023020108352089400_btz734-B37","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.ab.2007.10.012","article-title":"PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition","volume":"373","author":"Shen","year":"2008","journal-title":"Anal. Biochem"},{"key":"2023020108352089400_btz734-B38","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1021\/acs.jcim.6b00591","article-title":"Extreme gradient boosting as a method for quantitative structure-activity relationships","volume":"56","author":"Sheridan","year":"2016","journal-title":"J. Chem. Inf. 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