  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.
