Fuzzy logic based approaches for gene regulatory network inference

TL;DR

This paper reviews fuzzy logic and hybrid approaches for gene regulatory network inference, highlighting their development over the past two decades and their advantages in analyzing high-throughput biological data.

Contribution

It provides a comprehensive review of fuzzy logic-based methods and hybrid approaches for gene regulatory network inference from high-throughput data.

Findings

Fuzzy logic approaches are extensively studied for GRN inference.

Hybrid methods combining fuzzy logic with other techniques show promising results.

Fuzzy logic offers advantages like handling uncertainty and imprecision in biological data.

Abstract

The rapid advancement in high-throughput techniques has fueled the generation of large volume of biological data rapidly with low cost. Some of these techniques are microarray and next generation sequencing which provides genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These biological data are analyzed using computational techniques for knowledge discovery - which is one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays pivotal role in understanding gene regulation process and disease studies. From the last couple of decades, the researchers are interested in developing computational algorithms for GRN inference (GRNI) using high-throughput experimental data. Several computational approaches have been applied for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression based approaches, probabilistic approaches (Bayesian networks, naive byes), artificial neural networks, and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approach, is well studied in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches for GRNI developed during last two decades.