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迪杰拉蒂 - - 一个多路径并行混合深度学习框架,用于识别菌根PE/PPE蛋白
Fuyi Li1, Xudong Guo2, Yue Bi3
1College of Information Engineering, Northwest A&F University, Yangling, 712100, China; Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria, 3000, Australia.
Computers in biology and medicine
|June 25, 2023
概括
研究人员开发了Digerati,这是一种深度学习工具,用于快速准确地识别Mycobacterium tuberculosis中的PE和PPE蛋白质. 这一进步有助于理解宿主-病原体相互作用和毒性因素.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 结核菌菌的基因组中有10%的基因表征不佳,包括PE/PPE家族.
- PE/PPE蛋白涉及宿主-病原体相互作用,毒性和病原性.
- 遗传差异和PE/PPE蛋白质的重复性质挑战了传统的计算识别.
研究的目的:
- 开发一种先进的in silico方法,以有效识别和功能注释PE/PPE蛋白质.
- 推出Digerati,这是第一个基于深度学习的方法,用于快速准确地识别PE/PPE蛋白质.
主要方法:
- 使用多路径并行混合深度学习框架开发了Digerati.
- 集成的多层卷积神经网络,具有双向的,长的短期记忆.
- 包含了一个自我注意模块,用于学习PE/PPE蛋白的更高阶特征表示.
主要成果:
- 与基于对齐的方法 (BLASTP,PHMMER,HHsuite) 相比,Digerati表现显著改善 (18-20%更好).
- 在识别PE/PPE家族蛋白质方面取得了卓越的预测准确性和速度.
- 经验研究证实了深度学习方法的有效性.
结论:
- 迪杰拉蒂为识别PE/PPE蛋白提供了快速而准确的解决方案.
- 预计该工具将促进PE/PPE家庭成员的高通量分析.
- 迪杰拉蒂作为一个网络服务器和社区使用的源代码公开提供.
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