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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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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution.

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BioNet, a novel deep learning model, accurately predicts chemical-gene interactions (CGIs) using a graph encoder-decoder architecture. This computational approach accelerates drug screening by overcoming limitations of traditional experiments.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Network science

Background:

  • Predicting chemical-gene interactions (CGIs) is vital for drug screening.
  • Wet experiments for CGIs are costly and time-consuming.
  • Computational methods offer efficient in-silico exploration of CGIs.

Purpose of the Study:

  • To develop a data-driven model for predicting chemical-gene interactions.
  • To address the challenges of heterogeneous biological networks and large datasets.
  • To leverage graph neural networks for enhanced CGI prediction.

Main Methods:

  • Developed BioNet, a deep biological network model with a graph encoder-decoder architecture.
  • Utilized graph convolution for learning latent information from complex biological networks.
  • Employed tensor decomposition with the RESCAL algorithm for multi-type interaction predictions.
  • Implemented a parallel training algorithm on multiple GPUs for a large-scale network.

Main Results:

  • BioNet achieved an outstanding prediction performance with an Area Under the ROC Curve of 0.952.
  • The model significantly surpassed existing state-of-the-art methods.
  • Top predicted CGIs for cancer and COVID-19 were validated by external data and literature.

Conclusions:

  • BioNet demonstrates superior performance in predicting chemical-gene interactions.
  • The model's efficiency and accuracy can accelerate drug discovery and development.
  • BioNet provides a powerful tool for exploring complex biological networks.