Network inference in systems biology: recent developments, challenges, and applications.

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Gene regulatory network (GRN) inference from expression data is a complex, long-standing problem in computational biology.
• Despite over a decade of research and advancements, current inference algorithms remain imperfect.

Purpose of the Study:

• To review recent developments in gene regulatory network inference algorithms.
• To discuss persistent computational challenges and potential solutions.
• To highlight applications of network inference in cancer research and provide tool recommendations.

Main Methods:

• Review of state-of-the-art network inference algorithms (e.g., PIDC, Phixer).
• Discussion of computational challenges: algorithm combination, multi-source data integration, and pseudo-temporal ordering.
• Exploration of applications in cancer research.

Main Results:

• Identification of advanced algorithms and their limitations.
• Elucidation of key computational challenges hindering perfect GRN inference.
• Demonstration of network inference's utility in cancer research.

Conclusions:

• Gene regulatory network inference is an evolving field with ongoing challenges.
• Integration of diverse data sources and advanced algorithms is key to improving accuracy.
• Network inference holds significant promise for advancing cancer research and therapeutic strategies.