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相关概念视频

Overview of Metabolism01:40

Overview of Metabolism

Living cells constantly carry out various chemical reactions which are necessary for their proper functioning. These reactions are interlinked to one another via multiple pathways. The collection of these chemical reactions is known as metabolism.
Plant Metabolism
Sunlight, the primary source of energy in plants, is first absorbed by the chlorophyll pigments present in their leaves. Plants then use this energy to carry out photosynthesis, where water is oxidized into oxygen and carbon dioxide...
Regulation of Metabolism01:19

Regulation of Metabolism

Cellular needs and conditions vary from cell to cell and change within individual cells over time. For example, the required enzymes and energetic demands of stomach cells are different from those of fat storage cells, skin cells, blood cells, and nerve cells. Furthermore, a digestive cell works much harder to process and break down nutrients during the time that closely follows a meal compared with many hours after a meal. As these cellular demands and conditions vary, so do the amounts and...
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

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相关实验视频

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Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids
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用XGBoost进行多标签分类,用于代谢途径预测.

Hyunwhan Joe1, Hong-Gee Kim2,3

  • 1Biomedical Knowledge Engineering Lab., Seoul National University, Seoul, Republic of Korea.

BMC bioinformatics
|January 31, 2024
PubMed
概括

机器学习方法用于代谢途径预测,当应用分类修剪时,现在超过了传统方法,如PathoLogic. 一种新的基于XGBoost的方法mlXGPR,在单个生物体的基准指标上显示出优异的性能.

关键词:
生物循环生物循环代谢途径预测的预测在XGBoost中使用.

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科学领域:

  • 系统生物学 系统生物学
  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.

背景情况:

  • 代谢途径预测对于从基因组数据中重建生物体代谢网络至关重要.
  • 机器学习 (ML) 方法显示出有希望的结果,但以前的基于规则的方法表现不佳,例如PathoLogic.
  • 之前对PathoLogic的评估遗漏了分类学修剪,这对其性能产生了负面影响.

研究的目的:

  • 重新评估PathoLogic与分类学修剪并将其与ML方法进行比较.
  • 引入mlXGPR,一种改进的基于XGBoost的代谢途径预测方法.
  • 通过通过分类链结合标签相关性来增强ML路径预测.

主要方法:

  • 更新了PathoLogic的评估,包括分类学修剪.
  • 使用XGBoost和多标签分类框架开发mlXGPR.
  • 采用新的排名方法实施分类链,以利用标签相关性.
  • 对单个和多个生物体数据集的现有方法进行mlXGPR的基准测试.

主要成果:

  • 带有分类学修剪的病理学超越了以前的ML方法.
  • mlXGPR,特别是使用分类器链,超过了PathoLogic和其他ML方法.
  • 在单个生物基准上,mlXGPR在汉明损失,精度和F1得分方面实现了卓越的性能.

结论:

  • 基于ML的代谢途径预测可以实现高性能.
  • 优化的ML方法,如mlXGPR,可以超过已建立的工具,如PathoLogic与分类修剪.
  • 对ML方法的进一步改进对于竞争性代谢途径预测是可行的.