Fuzzy Systems in Bioinformatics and Computational Biology [electronic resource] /
Jin, Yaochu.editor.
Wang, Lipo.editor.
Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has to deal with a large amount of uncertainties. Fuzzy logic has shown to be a powerful tool in capturing different uncertainties in engineering systems. In recent years, fuzzy logic based modeling and analysis approaches are also becoming popular in analyzing biological data and modeling biological systems. Numerous research and application results have been reported that demonstrated the effectiveness of fuzzy logic in solving a wide range of biological problems found in bioinformatics, biomedical engineering, and computational biology. Contributed by leading experts world-wide, this edited book contains 16 chapters presenting representative research results on the application of fuzzy systems to genome sequence assembly, gene expression analysis, promoter analysis, cis-regulation logic analysis and synthesis, reconstruction of genetic and cellular networks, as well as biomedical problems, such as medical image processing, electrocardiogram data classification and anesthesia monitoring and control. This volume is a valuable reference for researchers, practitioners, as well as graduate students working in the field of bioinformstics, biomedical engineering and computational biology.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Induction of Fuzzy Rules by Means of Artificial Immune Systems in Bioinformatics -- Fuzzy Genome Sequence Assembly for Single and Environmental Genomes -- A Hybrid Promoter Analysis Methodology for Prokaryotic Genomes -- Fuzzy Vector Filters for cDNA Microarray Image Processing -- Microarray Data Analysis Using Fuzzy Clustering Algorithms -- Fuzzy Patterns and GCS Networks to Clustering Gene Expression Data -- Gene Expression Analysis by Fuzzy and Hybrid Fuzzy Classification -- Detecting Gene Regulatory Networks from Microarray Data Using Fuzzy Logic -- Fuzzy System Methods in Modeling Gene Expression and Analyzing Protein Networks -- Evolving a Fuzzy Rulebase to Model Gene Expression -- Infer Genetic/Transcriptional Regulatory Networks by Recognition of Microarray Gene Expression Patterns Using Adaptive Neuro-Fuzzy Inference Systems -- Scalable Dynamic Fuzzy Biomolecular Network Models for Large Scale Biology -- Fuzzy C-Means Techniques for Medical Image Segmentation -- Monitoring and Control of Anesthesia Using Multivariable Self-Organizing Fuzzy Logic Structure -- Interval Type-2 Fuzzy System for ECG Arrhythmic Classification -- Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks.
Biological systems are inherently stochastic and uncertain. Thus, research in bioinformatics, biomedical engineering and computational biology has to deal with a large amount of uncertainties. Fuzzy logic has shown to be a powerful tool in capturing different uncertainties in engineering systems. In recent years, fuzzy logic based modeling and analysis approaches are also becoming popular in analyzing biological data and modeling biological systems. Numerous research and application results have been reported that demonstrated the effectiveness of fuzzy logic in solving a wide range of biological problems found in bioinformatics, biomedical engineering, and computational biology. Contributed by leading experts world-wide, this edited book contains 16 chapters presenting representative research results on the application of fuzzy systems to genome sequence assembly, gene expression analysis, promoter analysis, cis-regulation logic analysis and synthesis, reconstruction of genetic and cellular networks, as well as biomedical problems, such as medical image processing, electrocardiogram data classification and anesthesia monitoring and control. This volume is a valuable reference for researchers, practitioners, as well as graduate students working in the field of bioinformstics, biomedical engineering and computational biology.
Computer-aided engineering.
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Computer-Aided Engineering (CAD, CAE) and Design.
Mathematical and Computational Engineering.
Artificial Intelligence.
Studies in fuzziness and soft computing