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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Hybrid Zones02:29

Hybrid Zones

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Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
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相关实验视频

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Isolation, Characterization, and Total DNA Extraction to Identify Endophytic Fungi in Mycoheterotrophic Plants
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使用混合变压器模型对真菌生物多样性的分类.

Madhurie Kumar Seth1, K Srinivas1, A Charan Kumari1

  • 1Dayalbagh Educational Institute, Dayalbagh, Agra 282005, Uttar Pradesh, India.

Journal of microbiological methods
|June 3, 2025
PubMed
概括

一个新的混合深度学习模型准确地分类真菌物种,帮助生物多样性和保护工作. 这种先进的技术提高了对农业和医学应用的真菌的理解.

科学领域:

  • 生态学和生物多样性
  • 计算生物学 计算生物学
  • 菌类学 菌类学是指菌类学.

背景情况:

  • 菌类对生态系统至关重要,影响营养循环,农业和医学.
  • 准确的真菌物种分类对于理解生物多样性和利用生态效益至关重要.
  • 现有的分类方法可以通过先进的计算技术来改进.

研究的目的:

  • 开发和评估用于多类真菌分类的混合深度学习模型.
  • 将视觉变压器和Swin变压器模型与转移学习框架合并.
  • 提高真菌物种识别的准确性和效率.

主要方法:

  • 利用了9115个真菌图像的数据集,跨越五种物种.
  • 使用数据增强来解决阶级不平衡.
  • 集成视觉变压器 (ViT) 和Swin变压器与MobileNetV2,DenseNet121和EfficientNetB0.0一起使用.
  • 应用了五倍交叉验证和Grad-CAM来实现模型的可解释性.

主要成果:

  • 与DenseNet121相结合的Swin变压器实现了最高的准确性 (96.96%的培训,95.97%的验证,95.57%的测试).
  • 混合模型展示了有效的概括,并最大限度地减少了错误分类.
关键词:
农业 农业 农业 农业生物多样性 生物多样性分类 分类 分类 分类.菌的真菌是一种.微生物学 微生物学变压器 变压器 变压器

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  • 格拉德-CAM可视化证实了模型的重点是生物相关的真菌结构.

    • 结论:

    • 混合深度学习模型为复杂的真菌分类任务提供了可扩展和高效的方法.
    • 这些模型平衡了局部特征建模与全球上下文捕获.
    • 这些发现支持改善真菌管理,生物多样性保护,可持续农业和医学诊断.