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      ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

      1 , 2 , 1 , 1 , 3 , 4 , 5 , 2
      Bioinformatics
      Oxford University Press (OUP)

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          Abstract

          Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides.

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          Author and article information

          Journal
          Bioinformatics
          Oxford University Press (OUP)
          1367-4803
          1460-2059
          June 01 2018
          June 01 2018
          Affiliations
          [1 ]School of Computer Science and Technology, Tianjin University, Tianjin, China
          [2 ]State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China
          [3 ]Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology
          [4 ]Monash Centre for Data Science, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia
          [5 ]School of Computer Software, Tianjin University, Tianjin, China
          Article
          10.1093/bioinformatics/bty451
          6247924
          29868903
          8461f577-c89b-43ee-9df7-71e5194b012b
          © 2018

          https://academic.oup.com/journals/pages/about_us/legal/notices

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