Identification of signal peptide sequences based on mean value matrix image

DOI

10.2991/mecs-17.2017.124How to use a DOI?

Keywords

Signal peptides, Secretory, Mean Value Matrix, Fuzzy K-nearest neighbor.

Abstract

Signal peptides are short peptide chains, which become a key tool in revising cells for gene therapy or developing new drugs. But for predicting the signal peptide sequences, the preliminary step is to distinguish whether the protein sequence is a secretory or non-secretory sequence. The sample with a pseudo amino acid (PseAA) composition representation of the protein sequence so as to incorporate a plentiful amount of protein sequence pattern information in order to increase the prediction precision for the classification. This article, which is based on the value of hydrophobicity, hydrophilicity, side-chain mass for sequence, we put forward a new method -- protein mean value matrix image(MVMI) to predict types from secretory and non-secretory protein sequences. Two geometric moments are on the base of the protein MVMI were collected from each of the protein sequences are made for their PseAA. It could testify by means of the jackknife cross-check method that the total successful rate are over 97%. The experimental results indicate that bioinformatics based on theory methodology can simplify and make experimental studies more intuitive.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xuexuan Zhu
AU  - Chuncai Xiao
PY  - 2016/06
DA  - 2016/06
TI  - Identification of signal peptide sequences based on mean value matrix image
BT  - Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SP  - 135
EP  - 140
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.124
DO  - 10.2991/mecs-17.2017.124
ID  - Zhu2016/06
ER  -