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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Alternative drug dissolution methods include the rotating bottle, intrinsic dissolution test, peristalsis, and the Franz diffusion cell method. The rotating bottle method involves meticulously rotating tightly capped controlled-release beads in a temperature-controlled bath. Periodic decanting of samples allows for residue assay, followed by refilling with fresh medium and testing at various pH levels to emulate the gastrointestinal tract conditions.In contrast, the intrinsic dissolution test...
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Drug repositioning by belief networks and ensemble method.

Manh Hung Le1, Nam Anh Dao1, Xuan Tho Dang2

  • 1Electric Power University, Ha Noi, Vietnam.

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Summary

Drug repositioning accelerates pharmaceutical development. This study introduces a belief network model that enhances drug-disease association predictions, improving accuracy and identifying new drug candidates for complex diseases.

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belief networkdrug repositioningdrug-disease predictionensemble votinggraph path

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Area of Science:

  • Computational Biology
  • Pharmacology
  • Bioinformatics

Background:

  • Drug repositioning offers cost and time efficiencies in pharmaceutical development.
  • Machine learning models are increasingly used for drug discovery but face challenges in complex biological networks.
  • Accurate prediction of drug-disease relationships is crucial for effective drug repositioning.

Purpose of the Study:

  • To optimize predictive performance of drug-disease relationship models.
  • To identify potential drug-disease connections using a belief network framework.
  • To enhance the accuracy and reliability of drug repositioning strategies.

Main Methods:

  • Developed a belief network framework to model drug-disease-protein interactions.
  • Selected optimal graph paths for drug-disease pairs to prioritize biological interactions.
  • Employed an ensemble of four classification methods with a voting mechanism for robustness.

Main Results:

  • The proposed analytical framework demonstrated superior performance and high reliability in identifying drug-disease associations.
  • The model effectively prioritized critical biological interactions for improved prediction accuracy.
  • Case studies confirmed the model's ability to discover promising drug candidates for challenging diseases.

Conclusions:

  • The belief network approach significantly enhances drug-disease relationship prediction.
  • This method offers a reliable and adaptable tool for drug repositioning research.
  • The framework holds practical significance for discovering novel therapeutic applications of existing drugs.