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Rapidly dividing tumors, embryos, and wounded tissues require more oxygen than usual, lowering the oxygen concentration in the blood. At low oxygen or hypoxic conditions, an oxygen-sensitive transcription factor called the hypoxia-inducible factor 1 or HIF1 is activated. HIF1 is a dimeric protein of alpha (ɑ) and beta (β) subunits.  Under optimal oxygen conditions, HIF1β is present in the nucleus while HIF1ɑ remains in the cytosol. HIF1ɑ is hydroxylated by prolyl...
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A Simple Bioassay for the Evaluation of Vascular Endothelial Growth Factors
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DeepStackVEGF是一个堆叠集团深度学习框架,用于血管内皮生长因子预测.

Farman Ali1, Majdi Khalid2, Abdulmohsen Algarni3

  • 1Department of Computer Science, Bahria University, Islamabad, 44000, Pakistan. farman335@yahoo.com.

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PubMed
概括
此摘要是机器生成的。

使用深度学习,DeepStack-VEGF准确地预测了血管内皮生长因子 (VEGF). 这种计算方法有助于开发抗血管生成疗法和推进精准医学.

关键词:
深度学习是一种深度学习.预先训练的语言模型.堆叠集体学习 堆叠集体学习

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科学领域:

  • 生物技术是生物技术.
  • 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:

  • 血管内皮生长因子 (VEGF) 对血管生成至关重要,影响伤口愈合,组织修复,骨形成以及癌症和糖尿病视网膜病变等疾病.
  • VEGF是抗血管生成药物和精密医学的关键治疗标.
  • 目前用于VEGF识别的实验方法昂贵且耗时,需要高效的计算解决方案.

研究的目的:

  • 开发DeepStack-VEGF,这是一个深度学习框架,用于准确和可靠地预测VEGF.
  • 整合多样化的序列衍生功能和预训练的嵌入,以增强预测能力.
  • 验证拟议的计算框架在支持药物发现和精准医学方面的有效性.

主要方法:

  • 序列衍生特征的整合:物理化学描述符,序列模式,进化信息和次要结构动机.
  • 使用来自UniProt和ProtBert的预训练嵌入式.
  • 使用支持矢量机器-递归特征消除进行特征优化.
  • 采用反生成对抗网络,门式循环单元和囊卷积神经网络架构的堆叠组合.

主要成果:

  • 合并的特征集和堆叠组合显著超过了单个模型的性能.
  • 在VEGF预测中实现了卓越的准确性,稳定性和概括性.
  • 证明了将深度学习与生物见解相结合的有效性.

结论:

  • DeepStack-VEGF为VEGF识别提供了一个可靠和可扩展的计算框架.
  • 开发的模型支持合理的药物发现和抗血管生成疗法的设计.
  • 这种方法通过实现高效的VEGF分析来推进精密医学应用.