{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:18:49Z","timestamp":1783948729096,"version":"3.55.0"},"reference-count":75,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T00:00:00Z","timestamp":1544400000000},"content-version":"vor","delay-in-days":343,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572506"],"award-info":[{"award-number":["61572506"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61722212"],"award-info":[{"award-number":["61722212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>\n                    Protein\u2010protein interactions (PPIs), as an important molecular process within cells, are of pivotal importance in the biochemical function of cells. Although high\u2010throughput experimental techniques have matured, enabling researchers to detect large amounts of PPIs, it has unavoidable disadvantages, such as having a high cost and being time consuming. Recent studies have demonstrated that PPIs can be efficiently detected by computational methods. Therefore, in this study, we propose a novel computational method to predict PPIs using only protein sequence information. This method was developed based on a deep learning algorithm\u2010stacked sparse autoencoder (SSAE) combined with a Legendre moment (LM) feature extraction technique. Finally, a probabilistic classification vector machine (PCVM) classifier is used to implement PPI prediction. The proposed method was performed on human, unbalanced\u2010human,\n                    <jats:italic>H. pylori<\/jats:italic>\n                    , and\n                    <jats:italic>S. cerevisiae<\/jats:italic>\n                    datasets with 5\u2010fold cross\u2010validation and yielded very high predictive accuracies of 98.58%, 97.71%, 93.76%, and 96.55%, respectively. To further evaluate the performance of our method, we compare it with the support vector machine\u2010 (SVM\u2010) based method. The experimental results indicate that the PCVM\u2010based method is obviously preferable to the SVM\u2010based method. Our results have proven that the proposed method is practical, effective, and robust.\n                  <\/jats:p>","DOI":"10.1155\/2018\/4216813","type":"journal-article","created":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T18:31:37Z","timestamp":1544466697000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Predicting Protein Interactions Using a Deep Learning Method\u2010Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1682-5712","authenticated-orcid":false,"given":"Yanbin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5296-2066","authenticated-orcid":false,"given":"Zhuhong","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7785-929X","authenticated-orcid":false,"given":"Liping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4797-2902","authenticated-orcid":false,"given":"Tonghai","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,12,10]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr930"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/29.1.242"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/29.1.239"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1006\/meth.2001.1183"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/bfgp\/elm035"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1021\/pr050139e"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti532"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"KotsireasI. 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