International Journal of Soft Computing

325
Views

0
Downloads

An Efficient Method Based on Hopfield Neural Network for RNA Secondary Structure Prediction

YanQiu Che , Qiping Cao and Zheng Tang
Page: 61-66 | Received 21 Sep 2022, Published online: 21 Sep 2022

By Research Area

Medicine & Public Health Life Sciences Engineering Mathematics Biomedicine Physics Chemistry Computer Science Earth Sciences Social Sciences Business and Management Psychology Materials Science Economics Education Environment Philosophy Statistics Law Political Science and International Relations Pharmacy Dentistry Energy Linguistics Geography Finance Criminology and Criminal Justice Medicine Cultural and Media Studies History Architecture / Design Literature Biomedical Sciences Religious Studies Education & Language Food Science & Nutrition Public Health

By Volume and Issue

Abstract

We present an improved method based on the Hopfield neural network for RNA secondary structure prediction in this study. The proposed method adjusts two parameters of the energy function in gradient ascent direction when the Hopfield neural network traps in a local minimum. The correction of the two parameters can increase the energy temporarily and help the network escape from the local minimum. The proposed algorithm was analyzed theoretically and evaluated experimentally through predicting RNA secondary structure. The simulation results on four RNA sequence show that the proposed algorithm performs better than others and has the ability to search the more stable RNA secondary structure for a RNA sequence.

How to cite this article:

YanQiu Che , Qiping Cao and Zheng Tang . An Efficient Method Based on Hopfield Neural Network for RNA Secondary Structure Prediction.
DOI: https://doi.org/10.36478/ijscomp.2006.61.66
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2006.61.66