期刊文献+

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences 被引量:12

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences
下载PDF
导出
摘要 Pse-in-One 2.0 is a package of web-servers evolved from Pse-in-One (Liu, B., Liu, F., Wang, X., Chen, J. Fang, L. & Chou, K.C. Nucleic Acids Research, 2015, 43:W65-W71). In order to make it more flexible and comprehensive as suggested by many users, the updated package has incorporated 23 new pseudo component modes as well as a series of new feature analysis approaches. It is available at http://bioinformatics.hitsz.edu.cn/Pse-in-One2.0/. Moreover, to maximize the convenience of users, provided is also the stand-alone version called “Pse-in-One-Analysis”, by which users can significantly speed up the analysis of massive sequences. Pse-in-One 2.0 is a package of web-servers evolved from Pse-in-One (Liu, B., Liu, F., Wang, X., Chen, J. Fang, L. & Chou, K.C. Nucleic Acids Research, 2015, 43:W65-W71). In order to make it more flexible and comprehensive as suggested by many users, the updated package has incorporated 23 new pseudo component modes as well as a series of new feature analysis approaches. It is available at http://bioinformatics.hitsz.edu.cn/Pse-in-One2.0/. Moreover, to maximize the convenience of users, provided is also the stand-alone version called “Pse-in-One-Analysis”, by which users can significantly speed up the analysis of massive sequences.
出处 《Natural Science》 2017年第4期67-91,共25页 自然科学期刊(英文)
关键词 PSEUDO COMPONENTS DNA SEQUENCES RNA SEQUENCES Protein SEQUENCES Pseudo Components DNA Sequences RNA Sequences Protein Sequences
  • 相关文献

参考文献4

二级参考文献12

  • 1BHASIN M, RAGHAVA G. GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors[J]. Nueleie Acids Research, 2004, 32:383- 389.
  • 2FLIZOLA M, WENISTEN H. The study of G-protein coupled receptor oligomerization with computational modeling and bioinformatics[J]. FEBS Journal, 2005,272: 2926-2938.
  • 3CHOU KC. Prediction of G-protein-coupled receptor ctasses [J]. Proteome Research, 2005, 4(4): 1413-1418.
  • 4KURGAN L A, HOMAEIAN L. Prediction ot structural classes for protein sequences and domalns--Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy [J]. Pattern Recognition, 2006, 39 (12) : 2323-2343.
  • 5ZHANG T L, DINGY S, CHOU K C. Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence [J]. Computational Biology and Chemistry, 2006, 30:367-377.
  • 6CHOU K C. Prediction of protein celluar attributes using pseudo amino acid composition [J]. Proteins, 2001, 43 (3) : 246-252.
  • 7DINGY S, ZHANG T L,CHOU K C. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network [J]. Protein & Peptide Letters,2007, 14:811-816.
  • 8ZHANG T L, DING Y S. Using pseudo amino acid composition and binary-tree support vector machines to prediet protein structural classes[J]. Amino Acids, 2007, 33:623-629.
  • 9ZHANG T L, DINGY S, SHAO S H. Protein subcellular location prediction based on pseudo amino acid composition and immune genetic algorithm [J]. Computational Intelligence and Bioinformatics,2006,411:534-540.
  • 10ZHANG T L, DING Y S, CHOU K C. Prediction protein structural classes with pseudo amino acid composition: Approximate entropy and hydrophobicity pattern [J]. Journal of Theoretical Biology, 2008, 250(1):186-193.

共引文献60

同被引文献44

引证文献12

二级引证文献23

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部