A Method for Bio-Sequence Analysis Algorithm Development Based on the PAR Platform

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2022-11-24 DOI:10.26599/BDMA.2022.9020030

Haipeng Shi;Huan Chen;Qinghong Yang;Jun Wang;Haihe Shi

引用次数: 0

Abstract

The problems of biological sequence analysis have great theoretical and practical value in modern bioinformatics. Numerous solving algorithms are used for these problems, and complex similarities and differences exist among these algorithms for the same problem, causing difficulty for researchers to select the appropriate one. To address this situation, combined with the formal partition-and-recur method, component technology, domain engineering, and generic programming, the paper presents a method for the development of a family of biological sequence analysis algorithms. It designs highly trustworthy reusable domain algorithm components and further assembles them to generate specifific biological sequence analysis algorithms. The experiment of the development of a dynamic programming based LCS algorithm family shows the proposed method enables the improvement of the reliability, understandability, and development efficiency of particular algorithms.

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一种基于标准杆数平台的生物序列分析算法开发方法

生物序列分析问题在现代生物信息学中具有重要的理论和实践价值。这些问题使用了大量的求解算法，而对于同一个问题，这些算法之间存在着复杂的相似性和差异性，这给研究人员选择合适的算法带来了困难。针对这种情况，结合形式划分和递归方法、组件技术、领域工程和通用程序设计，本文提出了一种开发生物序列分析算法家族的方法。它设计了高度可信的可重复使用的领域算法组件，并进一步组装它们以生成特定的生物序列分析算法。基于动态规划的LCS算法族的开发实验表明，该方法能够提高特定算法的可靠性、可理解性和开发效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.