SymNOM-GED: Symmetric neighbor outlier mining in gene expression datasets

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2024-06-23 DOI:10.1016/j.jocs.2024.102365

Bikash Baruah , Manash P. Dutta , Subhasish Banerjee , Dhruba K. Bhattacharyya

引用次数: 0

Abstract

The accurate detection of outliers in gene expression datasets plays a crucial role in the unraveling of intricate biological processes. This research introduces "SymNOM-GED," an innovative algorithm for outlier mining in gene expression datasets, with a focus on Esophageal Squamous Cell Carcinoma (ESCC). SymNOM-GED leverages symmetric neighbor to effectively identify outliers by considering local and global gene expression patterns. Extensive experiments demonstrate that SymNOM-GED outperforms existing algorithms in terms of accuracy, robustness, and scalability. The algorithm's performance is validated using clustering coefficient, graph density, and modularity, confirming its superiority. SymNOM-GED's precise and reliable outlier detection capabilities contribute significantly to bioinformatics research, offering insights into gene expression patterns in diverse biological contexts.

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SymNOM-GED：基因表达数据集中的对称邻域异常值挖掘

准确检测基因表达数据集中的异常值对揭示错综复杂的生物过程起着至关重要的作用。本研究介绍了 "SymNOM-GED"，这是一种在基因表达数据集中挖掘异常值的创新算法，重点关注食管鳞状细胞癌（ESCC）。SymNOM-GED 利用对称邻接法，通过考虑局部和全局基因表达模式，有效地识别异常值。大量实验证明，SymNOM-GED 在准确性、鲁棒性和可扩展性方面都优于现有算法。该算法的性能通过聚类系数、图密度和模块性得到了验证，证实了其优越性。SymNOM-GED 精确可靠的离群点检测能力为生物信息学研究做出了重大贡献，为深入了解不同生物背景下的基因表达模式提供了依据。

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

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).