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Protein Networks

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Half-Space Proximal Networks (HSPNs): A Proxy for Multi-Query Similarity Searching Models Predicting Tumor-Homing

Maylin Romero¹, Yovani Marrero-Ponce^2,3,4, Felix Martinez-Rios²

¹School of Chemical Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador.

|November 24, 2025

Summary

A novel machine learning method using network science effectively predicts tumor-homing peptides (THPs). This approach surpasses existing methods, offering a reliable tool for identifying THPs for cancer treatment applications.

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Area of Science:

Computational chemistry
Bioinformatics
Machine learning

Background:

Tumor-homing peptides (THPs) are crucial for targeted cancer therapies.
Developing accurate methods for THP identification is essential for advancing cancer treatment.

Purpose of the Study:

To present a novel, nontrained machine learning (ML) method for predicting THPs.
To leverage network science and multiquery similarity searching for enhanced THP identification.

Main Methods:

Utilized a half-space proximal network (HSPN) to represent the chemical space of THPs.
Employed centrality measures to identify significant THPs for similarity searches.
Developed multiquery similarity-based search models (MQSSMs) using identified THPs as queries.

Main Results:

MQSSMs derived from HSPNs (THP2) showed superior performance over classical chemical space networks (THP1).
Exceptional performance (MCC > 0.887) was achieved with MQSSMs (THP3) using both CSN and HSPN derived queries.
The proposed model (THP3) outperformed state-of-the-art supervised ML methods and existing THP prediction servers.

Conclusions:

Network-based similarity searches are highly effective and reliable for identifying THPs.
The developed ML method offers a significant advancement in THP prediction for cancer therapy.
This study highlights the potential of network science in drug discovery and development.