Fuzzy Logic and Rule-Based Methods in Bioinformatics

Abstract

This chapter reviews some fuzzy logic and rule-based approaches in bioinformatics. Among the fuzzy approaches, we emphasize fuzzy neural networks (FNN), which have advantages from both fuzzy logic (e.g., linguistic rules and reduced computation) and neural networks (e.g., ability to learn from data and universal approximation). After the overview in Sect. 15.1, the structure and algorithm of the FNN are reviewed in Sect. 15.2. In Sect. 15.3, we describe a t-test-based gene importance ranking method followed by a description of how we use the FNN to classify three important microarray datasets, for lymphoma, small round blue cell tumor (SRBCT), and ovarian cancer (Sect. 15.4). Section 15.5 reviews various fuzzy and rule-based approaches to microarray data classification proposed by other authors, while Sect. 15.6 reviews fuzzy and rule-based approaches to clustering and prediction in microarray data. We discuss and draw some conclusions in Sect. 15.7.