Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. We have developed an automated method for predicting signal peptide sequences and their cleavage sites in eukaryotic and bacterial protein sequences. It is a 2-layer predictor: the 1st-layer prediction engine is to identify a query protein as secretory or non-secretory; if it is secretory, the process will be automatically continued with the 2nd-layer prediction engine to further identify the cleavage site of its signal peptide. The new predictor is called Signal-CF, where C stands for ”coupling” and F for ”fusion”, meaning that Signal-CF is formed by incorporating the subsite coupling effects along a protein sequence and by fusing the results derived from many width-different scaled windows through a voting system. Signal-CF is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-CF is freely available as a web-server at or

References in zbMATH (referenced in 30 articles )

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  1. Georgiou, D. N.; Karakasidis, T. E.; Megaritis, A. C.; Nieto, Juan J.; Torres, A.: An extension of fuzzy topological approach for comparison of genetic sequences (2015)
  2. Massahi, Aslan; Çalık, Pınar: In-silico determination of \textitPichiapastoris signal peptides for extracellular recombinant protein production (2015)
  3. Chen, Xiao; Peng, Qinke; Han, Libin; Zhong, Tao; Xu, Tao: An effective haplotype assembly algorithm based on hypergraph partitioning (2014)
  4. Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan: R3P-Loc: a compact multi-label predictor using ridge regression and random projection for protein subcellular localization (2014)
  5. Xu, Yan; Wang, Xiaobo; Wang, Yongcui; Tian, Yingjie; Shao, Xiaojian; Wu, Ling-Yun; Deng, Naiyang: Prediction of posttranslational modification sites from amino acid sequences with kernel methods (2014)
  6. Mishra, Pooja; Nath Pandey, Paras: Elman RNN based classification of proteins sequences on account of their mutual information (2012)
  7. Cheng, Feng; Theodorescu, Dan; Schulman, Ira G.; Lee, Jae K.: \textitInvitro transcriptomic prediction of hepatotoxicity for early drug discovery (2011)
  8. Kavousi, Kaveh; Moshiri, Behzad; Sadeghi, Mehdi; Araabi, Babak N.; Moosavi-Movahedi, Ali Akbar: A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM (2011)
  9. Esmaeili, Maryam; Mohabatkar, Hassan; Mohsenzadeh, Sasan: Using the concept of Chou’s pseudo amino acid composition for risk type prediction of human papillomaviruses (2010)
  10. Georgiou, D. N.; Karakasidis, T. E.; Nieto, Juan J.; Torres, A.: A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets (2010)
  11. Huang, Wei; Zhang, Jianmin; Wang, Yurong; Huang, Dan: A simple method to analyze the similarity of biological sequences based on the fuzzy theory (2010)
  12. Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra: High performance set of PseAAC and sequence based descriptors for protein classification (2010)
  13. Nanni, Loris; Shi, Jian-Yu; Brahnam, Sheryl; Lumini, Alessandra: Protein classification using texture descriptors extracted from the protein backbone image (2010)
  14. Yang, Jie; Li, Jia-Huang; Wang, Jin; Zhang, Chen-Yu: Molecular modeling of BAD complex resided in a mitochondrion integrating glycolysis and apoptosis (2010)
  15. Yang, Lianping; Zhang, Xiangde; Wang, Tianming: The Burrows-Wheeler similarity distribution between biological sequences based on Burrows-Wheeler transform (2010)
  16. Yu, Lezheng; Guo, Yanzhi; Li, Yizhou; Li, Gongbing; Li, Menglong; Luo, Jiesi; Xiong, Wenjia; Qin, Wenli: SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition (2010)
  17. Anand, Ashish; Suganthan, P. N.: Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates (2009)
  18. Choo, Khar Heng; Tan, Tin Wee; Ranganathan, Shoba: A comprehensive assessment of N-terminal signal peptides prediction methods (2009) ioport
  19. Frenkel, Zakharia M.; Frenkel, Zeev M.; Trifonov, Edward N.; Snir, Sagi: Structural relatedness via flow networks in protein sequence space (2009)
  20. Georgiou, D. N.; Karakasidis, T. E.; Nieto, J. J.; Torres, A.: Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou’s pseudo amino acid composition (2009)

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