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In order to select the important features with strong classification ability, we proposed a recursive feature selection with random forest to improve protein structural class prediction. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed feature selection method effectively improves the efficiency of protein structural class prediction. Only less than 5% features are used, but the prediction accuracy is improved by 4.6-13.3%. We further compared different protein features and found that the predicted secondary structural features achieve the best performance. This understanding can be used to design more powerful prediction methods for the protein structural class.<\/jats:p>","DOI":"10.1155\/2021\/5529389","type":"journal-article","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T19:21:44Z","timestamp":1620501704000},"page":"1-9","source":"Crossref","is-referenced-by-count":30,"title":["Using Recursive Feature Selection with Random Forest to Improve Protein Structural Class Prediction for Low-Similarity Sequences"],"prefix":"10.1155","volume":"2021","author":[{"given":"Yaoxin","family":"Wang","sequence":"first","affiliation":[{"name":"College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Xu","sequence":"additional","affiliation":[{"name":"Qixin School, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Sciences, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2675-6511","authenticated-orcid":true,"given":"Qi","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1002\/bip.360250909"},{"key":"2","first-page":"455","article-title":"Structural bioinformatics and its impact to biomedical science and drug discovery","volume":"3","author":"K. 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