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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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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Deep Link-Prediction Based on the Local Structure of Bipartite Networks.

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  • 1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

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This study introduces a deep link-prediction (DLP) method using bipartite networks. DLP effectively leverages local network structures for improved relationship prediction, outperforming existing methods.

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Area of Science:

  • Network Science
  • Data Mining
  • Machine Learning

Background:

  • Bipartite networks are crucial for understanding hidden relationships and network evolution.
  • Current link prediction methods primarily rely on global network structures, neglecting local structural influences.

Purpose of the Study:

  • To propose a novel deep link-prediction (DLP) method that utilizes the local structure of bipartite networks.
  • To analyze the impact of local structural information on the accuracy of link prediction.

Main Methods:

  • Extracting local structural information between target nodes in bipartite networks.
  • Employing graph neural networks for representation learning of local structures to extract latent features.
  • Training a deep learning model on these latent features for link prediction.

Main Results:

  • The proposed DLP method demonstrated significant improvements over state-of-the-art link prediction techniques across five datasets.
  • Experimental analysis confirmed the critical role of local structure in enhancing link prediction accuracy.

Conclusions:

  • Leveraging local structures in bipartite networks offers a powerful approach for accurate link prediction.
  • The DLP method provides a new direction for analyzing network evolution and uncovering hidden relationships.