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Therefore, diagnosing AD in advance and discovering new drug targets are crucial, while these could be achieved by identifying AD-related proteins. The time-consuming and money-costing biological experiment makes researchers turn to develop more advanced algorithms to identify AD-related proteins.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Firstly, we proposed a hypothesis \u201csimilar diseases share similar related proteins\u201d. Therefore, five similarity calculation methods are introduced to find out others diseases which are similar to AD. Then, these diseases\u2019 related proteins could be obtained by public data set. Finally, these proteins are features of each disease and could be used to map their similarity to AD. We developed a novel method \u2018LRRGD\u2019 which combines Logistic Regression (LR) and Gradient Descent (GD) and borrows the idea of Random Forest (RF). LR is introduced to regress features to similarities. Borrowing the idea of RF, hundreds of LR models have been built by randomly selecting 40 features (proteins) each time. Here, GD is introduced to find out the optimal result. To avoid the drawback of local optimal solution, a good initial value is selected by some known AD-related proteins. Finally, 376 proteins are found to be related to AD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Three hundred eight of three hundred seventy-six proteins are the novel proteins. Three case studies are done to prove our method\u2019s effectiveness. These 308 proteins could give researchers a basis to do biological experiments to help treatment and diagnostic AD.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-019-3124-7","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T00:02:47Z","timestamp":1574640167000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Identifying Alzheimer\u2019s disease-related proteins by LRRGD"],"prefix":"10.1186","volume":"20","author":[{"given":"Tianyi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyi","family":"Zang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,25]]},"reference":[{"issue":"3","key":"3124_CR1","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.trci.2017.05.002","volume":"3","author":"J Cummings","year":"2017","unstructured":"Cummings J, Lee G, Mortsdorf T, Ritter A, Zhong K. 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