NeKo: A tool for automatic network construction from prior knowledge

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Biological networks are crucial for understanding molecular interactions and cellular functions.
• Manual construction of these networks is time-consuming and labor-intensive.

Purpose of the Study:

• To introduce NeKo, a Python package designed to automate the construction of biological networks.
• To provide researchers with a flexible and efficient tool for integrating and prioritizing molecular interactions.

Main Methods:

• NeKo integrates molecular interaction data from various public databases.
• It allows users to specify molecules of interest (genes, proteins, phosphosites).
• Users can apply flexible filtering strategies (e.g., direct/indirect, signed/unsigned interactions).

Main Results:

• NeKo automates the time-consuming process of biological network construction.
• Demonstrated utility in constructing networks from transcriptomics data (medulloblastoma) and modeling drug synergies.
• Provides a streamlined and efficient approach to network building.

Conclusions:

• NeKo enhances accessibility and efficiency for researchers in constructing biological networks.
• The package facilitates deeper insights into cellular functions and biological processes through automated network analysis.