Applications of fuzzy logic in genomics

Advances in techniques for high throughput data gathering such as microarrays and DNA sequencing machines have opened up new research avenues in genomics. Large-scale biological research such as genome projects are now producing enormous quantities of genomic data using these rapidly growing technologies. Transforming the massive data to useful biological knowledge is the present challenge. Different analysis tools are being developed in order to detect and understand the phenomena of gene regulation and physiological functions and assessing the quality of a genomic sequence. Fuzzy systems are suitable for uncertain or approximate reasoning when systems are difficult to describe with a mathematical model. They allow problem solving and decision making with incomplete or uncertain information. This unique feature makes them an ideal tool for analyzing complex genomic data. This paper presents application of fuzzy systems in (1) developing a confidence measure to assess the accuracy of bases called by a DNA basecalling algorithm, and (2) building a gene interaction model that identifies triplets of activators, repressors, and targets in gene expression data. It is shown that applying appropriate fuzzy conjunction and aggregation rule increases the resilience of the fuzzy gene interaction model to noise.