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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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相关实验视频

Updated: Jun 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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基于复杂值的灵活神经树和负样本选择算法的糖尿病复合关系识别.

Xiaochao Sun1, Bin Yang2

  • 1Library, Zaozhuang University, Zaozhuang, 277160, China.

Current computer-aided drug design
|June 11, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用复杂值灵活神经树 (CVFNT) 模型和负样本选择的新方法,以准确地选草药中的糖尿病化合物用于网络药理学. 这种方法改善了化合物选择,以便更好地进行治疗分析.

关键词:
具有复杂价值的灵活神经树糖尿病 糖尿病患者 糖尿病患者负样本选择 负样本选择网络药理学 网络药理学虚拟选是虚拟的选.

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科学领域:

  • 计算化学是一种计算化学.
  • 药理学 药理学是指药理学的学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 虚拟查 (VS) 在网络药理学中至关重要,用于从大型化合物库中识别潜在的候选药物.
  • 精确的化合物选对于可靠的网络构建,目标识别和药物发现途径分析至关重要.

研究的目的:

  • 在网络药理学中,提高选草药化合物用于糖尿病治疗的准确性.
  • 提出一种新的方法,将一个复杂值的灵活神经树 (CVFNT) 模型与负样本选择算法相结合.

主要方法:

  • 通过文献审查确定了与糖尿病相关的目标.
  • 在公共数据库中搜索与这些目标相关的活性化合物.
  • 开发了一个负样本选择算法,使用Tanimoto指数创建一组非活性化合物.
  • 采用优化的CVFNT模型来选有效的候选化合物.

主要成果:

  • 拟议的方法在各种指标 (TPR,FPR,精度,特异性,F1,AUC,ROC曲线) 上表现优于八种经典分类器.
  • 成功预测了Liangxue Sanyu Decoction中的18种化合物,这些化合物与糖尿病治疗有关.

结论:

  • 基于CVFNT的新方法显著提高了与糖尿病相关化合物虚拟查的准确性.
  • 这种方法提供了一个强大的工具,用于识别糖尿病管理中草药中的潜在治疗剂.