A novel coding scheme of nuclear receptor subfamilies prediction

DOI

10.2991/icmia-17.2017.41How to use a DOI?

Keywords

Nuclear receptor, Pseudo amino acid composition, Fuzzy K-nearest neighbor.

Abstract

Nuclear receptors (NRs) are important transcriptional regulators in animals. They regulate different functions, such as lipid, reproduction, carbohydrate metabolism, fibrosis and metabolism. NRs form a category of phylogenetically evolutional proteins and have been separated into diverse subfamilies on account of their domain function. But for predicting the subfamilies, the preliminary step is to distinguish whether the protein sequence is a nuclear receptor or non-nuclear receptor. 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 percentage of method to predict types from protein sequences of subfamilies. Three percentages are on the base of the physical and chemical properties 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 95%. 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  - Chun-Cai Xiao
PY  - 2017/06
DA  - 2017/06
TI  - A novel coding scheme of nuclear receptor subfamilies prediction
BT  - Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SP  - 224
EP  - 227
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.41
DO  - 10.2991/icmia-17.2017.41
ID  - Xiao2017/06
ER  -