|Autors: B, P. I., G, V. S.|
Title: Massively Parallel Algorithm for Multiple Sequence Alignment Based on Artificial Bee Colony
Keywords: Artificial Bee Colony, Bioinformatics, BlueGene/P, Computer Cluster, High Performance Computing, Multiple Sequence Alignment, Performance.
Abstract: In silico biological sequence processing is a key task in molecular biology. This scientific area requires powerful computing resources for exploring large sets of biological data. Parallel in silico simulations based on methods and algorithms for analysis of biological data using high-performance distributed computing is essential for accelerating the research and reducing the investment. Multiple sequence alignment is a widely used method for biological sequence processing. The goal of this method is DNA and protein sequences alignment. This paper presents an innovative parallel algorithm MSA_BG for multiple alignment of biological sequences that is highly scalable and locality aware. The MSA_BG algorithm we describe is iterative and is based on the concept of Artificial Bee Colony metaheuristics and the concept of algorithmic and architectural spaces correlation. The metaphor of the ABC metaheuristics has been constructed and the functionalities of the agents have been defined. The
Full text of the publication
1. Muhammad Ishaq, Asfandyar Khan, Mazliham Mohd Su’ud, Muhammad Mansoor Alam, Javed Iqbal Bangash, Abdullah Khan, "An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences", Computational and Mathematical Methods in Medicine, vol. 2022, Article ID 8691646, 11 pages, 2022. https://doi.org/10.1155/2022/8691646 - 2022 - в издания, индексирани в Scopus или Web of Science
Вид: публикация в международен форум