Network Inference in Systems Biology: Recent Developments, Challenges, and Applications

TL;DR

This paper reviews recent advances, challenges, and applications in gene regulatory network inference from expression data, highlighting new algorithms, computational hurdles, and applications in cancer research.

Contribution

It provides a comprehensive overview of state-of-the-art algorithms, discusses unresolved computational challenges, and explores applications in cancer research.

Findings

Review of recent algorithms like PIDC and Phixer

Identification of key computational challenges

Discussion of applications in cancer research

Abstract

One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.