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相关概念视频

Proteomics01:33

Proteomics

7.3K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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科伊纳:为蛋白质组学研究实现机器学习的民主化

Ludwig Lautenbacher1, Kevin L Yang2, Tobias Kockmann3

  • 1Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany.

bioRxiv : the preprint server for biology
|June 19, 2024
PubMed
概括
此摘要是机器生成的。

Koina是一个开源服务,使机器学习 (ML) 和深度学习 (DL) 模型可用于蛋白质组学. 这种集成增强了数据分析管道,改善了标识和光谱库生成.

关键词:
在生命科学中的AI.DDA DDA DDA DDA DDA DDA DDA DDA DDA DDA DDA DDA DDA DDA DDA DDA DDA时间 时间 时间 时间这是公平的,公平的.在PRM中,PRM是PRM.深度学习是一种深度学习.民主化 民主化 民主化美国联邦政府.机器学习是机器学习.质谱测量质谱测量质谱测量质谱测量质量测量质谱测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质属性预测和预测蛋白质组学 蛋白质组学网络服务 网络服务 网络服务

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科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 机器学习 (ML) 和深度学习 (DL) 为促进蛋白质组学应用提供了巨大的潜力,包括光谱库生成,标识和有针对性的获取.
  • 尽管经常发布新的ML/DL模型,但由于技术实施挑战,社区采用仍然很缓慢.

研究的目的:

  • 通过提高其可用性和可访问性来解决蛋白质组学中ML/DL模型的缓慢采用问题.
  • 开发和展示一个可访问的平台,将先进的ML/DL模型集成到现有的蛋白质组学工作流中.

主要方法:

  • 开发Koina,一个开源,容器化,分散的,在线可访问的高性能预测服务.
  • 将Koina与FragPipe计算平台集成,以展示其与现有蛋白质组学软件的兼容性.

主要成果:

  • 科伊纳可以将ML/DL模型无集成到各种蛋白质组学软件管道中.
  • 通过将Koina与FragPipe等现有工具集成,在数据分析方面取得了明显的改进.

结论:

  • 科伊纳促进更广泛的社区访问和利用蛋白质组学中最先进的ML / DL模型.
  • 开发的服务简化了先进的计算方法的应用,提高了蛋白质组学数据分析的效率和范围.