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

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

Many organic structures and gadgets are intrinsically fuzzy as their houses and behaviors comprise randomness or uncertainty. additionally, it has been proven that specific or optimum tools have major predicament in many bioinformatics difficulties. Fuzzy set conception and fuzzy good judgment are excellent to describe a few organic systems/objects and supply strong instruments for a few bioinformatics difficulties. This ebook comprehensively addresses numerous very important bioinformatics issues utilizing fuzzy recommendations and ways, together with dimension of ontological similarity, protein constitution prediction/analysis, and microarray information research. It additionally reports different bioinformatics functions utilizing fuzzy concepts.

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

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0 else ⎩ Besides their use in what is to come, fuzzy operators have been used extensively in multicriteria decision making [Bellman and Zadeh, 1970; Yager, 1988; Yager, 2004]. We end this section with an example of the use of fuzzy set operators in multicriteria decision making. 1. 4, to assess cancer risk based on the following observations. Suppose we decide that cancer risk should be high if either internal factors or environmental factors are high. This is modeled by a union operator (OR).

The limits as w → ∞ , generate the drastic union and intersection, defined by ⎧ A( x) if B( x) = 0 ⎪ ( A ∪d B)( x) = ⎨ B( x) if A( x) = 0 and ⎪ 1 else ⎩ ⎧ A( x) if B( x) = 1 ⎪ ( A ∩d B)( x) = ⎨ B( x) if A( x) = 1 . ⎪ 0 else ⎩ Besides their use in what is to come, fuzzy operators have been used extensively in multicriteria decision making [Bellman and Zadeh, 1970; Yager, 1988; Yager, 2004]. We end this section with an example of the use of fuzzy set operators in multicriteria decision making. 1.

An alternate formulation, denoted as a Takagi-Sugeno-Kang (TKS) system [Takagi and Sugeno, 1985; Sugeno and Kang, 1988] only modifies the membership functions in the consequent clause. It was developed for control applications where the output of the rule firing should be a function of the set of crisp input values. Instead of a general fuzzy set B of Y, the output of each rule is a specific function of the real inputs. The antecedent part of each rule, Ri, is matched as in the MA approach, but the output then becomes yi = Ai′1 × × Ain′ ( x1 ,…, xn ) ⋅ f i ( x1 ,…, xn ) .

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