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However, when traditional feature expression methods are adopted, the features usually contain considerable redundant information, which leads to a very low recognition rate of protein structural classes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The related source codes are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Xiaoheng-Wang12\/Wang-xiaoheng\/tree\/master\">https:\/\/github.com\/Xiaoheng-Wang12\/Wang-xiaoheng\/tree\/master<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Experimental verification of three low-similarity datasets suggests that the proposed model achieves notably good results as regarding the prediction of protein structural classes.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-019-3276-5","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T09:02:35Z","timestamp":1577178155000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion"],"prefix":"10.1186","volume":"20","author":[{"given":"Shunfang","family":"Wang","sequence":"first","affiliation":[]},{"given":"Xiaoheng","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"3276_CR1","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.2174\/0929867043364667","volume":"11","author":"KC Chou","year":"2004","unstructured":"Chou KC. 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