Fiscore package:有效的蛋白质结构数据可视化和探索

Auste Kanapeckaite
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引用次数: 0

摘要

缺乏生物信息学工具来快速评估蛋白质构象和拓扑特征,促使创建一个集成的和用户友好的R包。此外,Fiscore包实现了一个用于高斯混合建模的管道,使得非专家也可以很容易地使用这种机器学习方法。这一点尤其重要,因为概率机器学习技术可以帮助更好地解释复杂的生物现象,当有必要阐明可能在蛋白质功能中发挥作用的各种结构特征时。因此,Fiscore建立在蛋白质物理化学性质的数学公式上,可以帮助药物发现、目标评估或关系数据库的建立。此外,该包还提供交互式环境来探索各种感兴趣的特性。最后,这个包的目标之一是吸引结构生物信息学家,开发更强大和免费的R工具,可以帮助研究人员不一定是专门在这个领域。包Fiscore (v.0.1.3)通过CRAN和Github免费分发。
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Fiscore package: Effective protein structural data visualisation and exploration

The lack of bioinformatics tools to quickly assess protein conformational and topological features motivated to create an integrative and user-friendly R package. Moreover, the Fiscore package implements a pipeline for Gaussian mixture modelling making such machine learning methods readily accessible to non-experts. This is especially important since probabilistic machine learning techniques can help with a better interpretation of complex biological phenomena when it is necessary to elucidate various structural features that might play a role in protein function. Thus, Fiscore builds on the mathematical formulation of protein physicochemical properties that can aid in drug discovery, target evaluation, or relational database building. In addition, the package provides interactive environments to explore various features of interest. Finally, one of the goals of this package was to engage structural bioinformaticians and develop more robust and free R tools that could help researchers not necessarily specialising in this field. Package Fiscore (v.0.1.3) is distributed free of charge via CRAN and Github.

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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
自引率
0.00%
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0
审稿时长
15 days
期刊最新文献
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