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Machine learning techniques are playing key roles in this field. Typically, predictors based on machine learning techniques contain three main steps: feature extraction, predictor construction and performance evaluation. Although several Web servers and stand-alone tools have been developed to facilitate the biological sequence analysis, they only focus on individual step. In this regard, in this study a powerful Web server called BioSeq-Analysis (http:\/\/bioinformatics.hitsz.edu.cn\/BioSeq-Analysis\/) has been proposed to automatically complete the three main steps for constructing a predictor. The user only needs to upload the benchmark data set. BioSeq-Analysis can generate the optimized predictor based on the benchmark data set, and the performance measures can be reported as well. Furthermore, to maximize user\u2019s convenience, its stand-alone program was also released, which can be downloaded from http:\/\/bioinformatics.hitsz.edu.cn\/BioSeq-Analysis\/download\/, and can be directly run on Windows, Linux and UNIX. Applied to three sequence analysis tasks, experimental results showed that the predictors generated by BioSeq-Analysis even outperformed some state-of-the-art methods. It is anticipated that BioSeq-Analysis will become a useful tool for biological sequence analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbx165","type":"journal-article","created":{"date-parts":[[2017,11,15]],"date-time":"2017-11-15T12:14:17Z","timestamp":1510748057000},"page":"1280-1294","source":"Crossref","is-referenced-by-count":258,"title":["BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches"],"prefix":"10.1093","volume":"20","author":[{"given":"Bin","family":"Liu","sequence":"first","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2017,12,19]]},"reference":[{"issue":"1","key":"2019100807484994100_bbx165-B1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1093\/nar\/28.1.45","article-title":"The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000","volume":"28","author":"Bairoch","year":"2000","journal-title":"Nucleic Acids 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