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相关概念视频

Amino Acid Biosynthetic Pathways01:29

Amino Acid Biosynthetic Pathways

17
Amino acid biosynthesis is essential for cell growth, protein synthesis, and metabolic regulation. Cells generate essential and non-essential amino acids from metabolic intermediates to sustain vital biological functions. These intermediates originate from key metabolic pathways: glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway. Important precursors include α-ketoglutarate, pyruvate, oxaloacetate, phosphoenolpyruvate, and erythrose-4-phosphate, which...
17
Respiration Pathways01:26

Respiration Pathways

19
Cellular respiration is a fundamental metabolic process that enables organisms to generate energy from organic molecules. One of its central pathways is the tricarboxylic acid (TCA) cycle, also known as the Krebs cycle, which plays a crucial role in energy production and biosynthetic processes.Conversion of Pyruvate to Acetyl-CoAThe pyruvate generated from glycolysis undergoes oxidative decarboxylation by the pyruvate dehydrogenase complex, producing acetyl-CoA, one molecule of NADH, and one...
19
Introduction to Metabolism01:30

Introduction to Metabolism

47
Metabolism encompasses all biochemical reactions in a living organism, facilitating both the breakdown and synthesis of biomolecules. These metabolic processes are categorized into catabolic and anabolic pathways, which operate in a coordinated manner to ensure energy balance and cellular function.Catabolic Pathways and Energy ReleaseCatabolic pathways involve the breakdown of complex macromolecules such as carbohydrates, lipids, and proteins into smaller structures like monosaccharides, fatty...
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MVML-MPI:用于代谢途径推理的多视图多标签学习.

Xiaoyi Liu1, Hongpeng Yang1, Chengwei Ai2

  • 1Computer Science and Engineering, University of South Carolina, Columbia 29208, USA.

Briefings in bioinformatics
|November 6, 2023
PubMed
概括

我们开发了一个新的多视图多标签学习代谢途径推断 (MVML-MPI) 框架,以准确预测化合物参与代谢途径. 这种方法增强了药物发现和基因组规模的代谢模型开发.

关键词:
功能融合功能融合功能代谢途径的代谢途径分子表示的分子表示.多标签预测多标签预测多视图学习多视图学习

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科学领域:

  • 计算生物学是一种计算生物学.
  • 化学信息学 化学信息学
  • 系统生物学 系统生物学

背景情况:

  • 准确识别化合物参与代谢途径对于药物发现和构建基因组规模代谢模型 (GEM) 至关重要.
  • 现有的机器学习方法往往无法捕捉化合物的复杂,多方面的性质,导致不准确的路径预测.
  • 需要先进的计算框架来改善代谢途径推断.

研究的目的:

  • 引入一个新的框架,MVML-MPI (多视图多标签学习代谢途径推理),用于准确的代谢途径预测.
  • 通过有效地表示化合物特征及其与代谢途径的关系来解决当前方法的局限性.
  • 加强合成新化合物,药物向和开发GEM的战略.

主要方法:

  • MVML-MPI使用并行复合编码器来学习不同的表示和提取全面的特征.
  • 一个基于注意力的融合模块集成了多视图复合表示,捕捉复杂的相互依赖.
  • 该框架使用多标签学习来预测化合物可能参与的多个途径.

主要成果:

  • 与最先进的方法相比,MVML-MPI在基因和基因组通路数据集的京都百科全书上表现出更高的性能.
  • 该框架准确地表示化合物,并有效地捕捉化合物和代谢途径之间的复杂关系.
  • 实验结果验证了MVML-MPI在改善代谢途径推断方面的有效性.

结论:

  • MVML-MPI为代谢途径推断提供了强大而有效的解决方案,性能优于现有的方法.
  • 该框架具有很大的潜力,可以推进代谢途径的设计,帮助优化类似药物的化合物.
  • MVML-MPI促进了更准确和更全面的GEM的开发,支持各种生物研究应用.