PhaVIP: VIrion 蛋白质分类基于混沌游戏表示和视觉变压器
Jiayu Shang1, Cheng Peng1, Xubo Tang1
1Department of Electrical Engineering, City University of Hong Kong, Hong Kong (SAR), China.
Bioinformatics (Oxford, England)
|June 30, 2023
概括
一种新的计算方法,PhaVIP,使用视觉转换器和混乱游戏表示,准确地分类病毒蛋白 (PVPs). 该工具有助于理解菌体功能,并改善菌体分类学和宿主预测.
科学领域:
- * 生物信息学是一门学科.
- * 计算生物学 * 计算生物学
- * 病毒学 病毒学
背景情况:
- *菌体是生态系统中关键的细菌病毒,分析它们的蛋白质是理解它们作用的关键.
- *高通量测序产生了大量的菌体数据,但对菌体蛋白质,特别是病毒蛋白质 (PVPs) 的分类仍然是一个挑战.
- *目前用于PVP识别的实验方法昂贵且耗时,需要高效的计算方法.
研究的目的:
- * 开发一种快速而准确的计算方法来分类菌体病毒蛋白 (PVPs).
- * 标注PVP的特定类型,如囊蛋白和尾蛋白.
- *为了证明分类PVP在菌体分类和宿主预测等下游应用中的实用性.
主要方法:
- * 调整了视觉转换器,这是一个最先进的图像分类模型,用于PVP分类.
- * 编码蛋白质序列为独特的图像,使用混乱游戏表示.
- * 开发了PhaVIP,一种能够将序列分类为PVP或非PVP和注释PVP类型的方法.
主要成果:
- *PhaVIP在分类PVP方面表现出优异的性能,与各种数据集中的替代工具相比.
- * 该方法成功地分类了PVP类型,包括囊蛋白和尾蛋白.
- *将PhaVIP输出用于菌体分类学和宿主预测的应用,与使用所有蛋白质相比,产生了更好的结果.
结论:
- *PhaVIP提供了一个计算效率高,准确的解决方案,用于菌体病毒蛋白质的分类.
- * 该工具增强了菌体基因组和微生物组的分析.
- *PhaVIP促进了基应用的进步,包括分类学和宿主预测.
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