Novel approaches to develop community-built biological network models for potential drug discovery

Area of Science:

• Bioinformatics
• Drug Discovery
• Systems Biology

Background:

• Clinical trials generate vast molecular data using next-generation sequencing (NGS) and profiling.
• Interpreting this data requires a robust biological context, often provided by pathway tools and network models.
• The accuracy of insights derived from these tools depends heavily on their underlying biological content.

Purpose of the Study:

• To review the application of crowdsourcing in drug discovery for verifying and augmenting biological pathway tools and network models.
• To describe technologies enabling community-driven biological network construction.
• To highlight the potential of crowdsourcing for advancing biomarker discovery and personalized medicine.

Main Methods:

• Crowdsourcing for verification and augmentation of pathway tools and biological network models.
• Description of technologies for community-based biological network building.
• Leveraging expert crowds for the development and evaluation of biological network models.

Main Results:

• Crowdsourcing successfully verifies and enhances the biological content of pathway tools and network models.
• Community-driven approaches enable the creation of robust biological networks.
• Expert crowds can guide the entire development process of network models, ensuring mechanistic completeness.

Conclusions:

• Crowdsourcing ensures transparency and accuracy in biological content for data interpretation tools.
• This approach facilitates biomarker discovery and personalized medicine by mechanistically explaining patient variability.
• Expert crowds can accelerate the development and validation of biological network models for improved clinical applications.