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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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Updated: Jun 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Diabetes-compound Relationship Identification based on Complex-valued Flexible Neural Tree and Negative Sample

Xiaochao Sun1, Bin Yang2

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

Current Computer-Aided Drug Design
|June 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using a complex-valued flexible neural tree (CVFNT) model and negative sample selection to accurately screen diabetes compounds in herbs for network pharmacology. The approach improves compound selection for better treatment analysis.

Keywords:
Virtual screeningcomplex-valued flexible neural tree.data acquisitiondiabetesmulti-layer neural networknetwork pharmacology

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Bioinformatics

Background:

  • Virtual screening (VS) is crucial in network pharmacology for identifying potential drug candidates from large compound libraries.
  • Accurate compound screening is essential for reliable network construction, target identification, and pathway analysis in drug discovery.

Purpose of the Study:

  • To enhance the accuracy of screening herb compounds for diabetes treatment within network pharmacology.
  • To present a novel methodology combining a complex-valued flexible neural tree (CVFNT) model with a negative sample selection algorithm.

Main Methods:

  • Identified diabetes-related targets through literature review.
  • Searched public databases for active compounds linked to these targets.
  • Developed a negative sample selection algorithm using the Tanimoto index to create a set of inactive compounds.
  • Employed an optimized CVFNT model for screening effective candidate compounds.

Main Results:

  • The proposed method demonstrated superior performance compared to eight classical classifiers across various metrics (TPR, FPR, Precision, Specificity, F1, AUC, ROC curve).
  • Successfully predicted 18 compounds from Liangxue Sanyu Decoction with relevance to diabetes treatment.

Conclusions:

  • The novel CVFNT-based methodology significantly improves the accuracy of virtual screening for diabetes-related compounds.
  • This approach offers a robust tool for identifying potential therapeutic agents in herbal medicine for diabetes management.