Applications of fuzzy logic in bioinformatics by Dong Xu, James M Keller, Mihail Popescu, Rajkumar Bondugula PDF

By Dong Xu, James M Keller, Mihail Popescu, Rajkumar Bondugula

Many organic platforms and items are intrinsically fuzzy as their houses and behaviors comprise randomness or uncertainty. moreover, it has been proven that individual or optimum equipment have major quandary in many bioinformatics difficulties. Fuzzy set concept and fuzzy good judgment are excellent to describe a few organic systems/objects and supply sturdy instruments for a few bioinformatics difficulties. This booklet comprehensively addresses a number of vital bioinformatics issues utilizing fuzzy ideas and techniques, together with dimension of ontological similarity, protein constitution prediction/analysis, and microarray info research. It additionally studies different bioinformatics functions utilizing fuzzy thoughts.

Contents: creation to Bioinformatics; creation to Fuzzy Set concept and Fuzzy common sense; Fuzzy Similarities in Ontologies; Fuzzy good judgment in Structural Bioinformatics; program of Fuzzy common sense in Microarray information Analyses; different functions; precis and Outlook.

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Extra resources for Applications of fuzzy logic in bioinformatics

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After a discussion of the general principles of fuzzy set theory, membership functions and fuzzy connective operators, we focus on those areas for which we present specific applications within bioinformatics: fuzzy logic rule based systems, fuzzy clustering, fuzzy classifiers, particularly, the Fuzzy K-Nearest Neighbor algorithm, fuzzy measures and the fuzzy integral. , 1999] for more extensive development of the theory and selected applications. 1 Membership functions Traditional set theory is based on binary, or two-valued, logic.

Traditional crisp expert systems including those that manipulate numeric confidences or probabilities have been around for many years [Ignizio, 1991; Giarratano and Riley, 2005]. Fuzzy logic extends this approach by modeling linguistic propositions, rules and the inference procedure directly with fuzzy sets. In this section we describe 38 Applications of Fuzzy Logic in Bioinformatics the background necessary to understand and construct fuzzy logic inference systems for bioinformatics problems. Fuzzy logic begins with the concept of a linguistic variable [Zadeh, 1975a,b; Zadeh, 1976].

A characteristic function from X into the interval [0,1]. The value A(x) is called the membership of the point x in the fuzzy set A or the degree to which the point x belongs to the set A. For example, the fuzzy subset of “big positive even integers” could be defined by ⎧⎪ 2 1− if x = 2,4,6, A( x) = ⎨ x ⎪⎩ 0 else. An example closer to the topic of this book is a membership function, A, that describes the similarity of a protein sequence S to that of a specific sequence called S’ in a protein database.

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