You can view the full text of this article for free using the link below.

Title: Multi-agent list-based noising algorithm for protein structure prediction

Authors: Juan Lin; Yiwen Zhong; Ena Li

Addresses: College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China ' College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China; Key Laboratory of Smart Agriculture and Forestry (Fujian Agriculture and Forestry University), Fujian Province University, Fuzhou, Fujian 350002, China ' College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China

Abstract: Protein structure prediction (PSP) with ab initio model problem is still a challenge in bioinformatics on account of high computational complexity. To solve this problem within a limited time and resource, a multi-agent list-based nosing (MLBN) algorithm is presented. MLBN contains three main features. First, a flexible noising list is designed to adjust the solution acceptance condition according to the convergence. An adaptive multiple sampling strategy is included to provide a strong exploitation. A parallel framework explores the searching space in a more effective way. Compared to traditional Simulated Annealing (SA) algorithm, MLBN introduces only one extra parameter for the length of noising list and it is insensitive to specific problems. Conducted experiments in a range of protein sequences indicate MLBN performs better than, or at least is comparable with, several state-of-the-art algorithms for PSP.

Keywords: nosing method; list-based; adaptive sampling; protein structure prediction.

DOI: 10.1504/IJWMC.2020.104771

International Journal of Wireless and Mobile Computing, 2020 Vol.18 No.1, pp.90 - 100

Received: 14 Nov 2018
Accepted: 19 Jul 2019

Published online: 28 Jan 2020 *

Full-text access for editors Access for subscribers Free access Comment on this article