Fuzzy systems in bioinformatics and computational biology

Filippo Menolascina, Vitoantonio Bevilacqua (auth.), Yaochu Jin, Lipo Wang (eds.)9783540899679, 3540899677

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.

Table of contents :
Front Matter….Pages –
Induction of Fuzzy Rules by Means of Artificial Immune Systems in Bioinformatics….Pages 1-17
Fuzzy Genome Sequence Assembly for Single and Environmental Genomes….Pages 19-44
A Hybrid Promoter Analysis Methodology for Prokaryotic Genomes….Pages 45-65
Fuzzy Vector Filters for cDNA Microarray Image Processing….Pages 67-82
Microarray Data Analysis Using Fuzzy Clustering Algorithms….Pages 83-102
Fuzzy Patterns and GCS Networks to Clustering Gene Expression Data….Pages 103-125
Gene Expression Analysis by Fuzzy and Hybrid Fuzzy Classification….Pages 127-140
Detecting Gene Regulatory Networks from Microarray Data Using Fuzzy Logic….Pages 141-163
Fuzzy System Methods in Modeling Gene Expression and Analyzing Protein Networks….Pages 165-189
Evolving a Fuzzy Rulebase to Model Gene Expression….Pages 191-215
Infer Genetic/Transcriptional Regulatory Networks by Recognition of Microarray Gene Expression Patterns Using Adaptive Neuro-Fuzzy Inference Systems….Pages 217-233
Scalable Dynamic Fuzzy Biomolecular Network Models for Large Scale Biology….Pages 235-255
Fuzzy C-Means Techniques for Medical Image Segmentation….Pages 257-271
Monitoring and Control of Anesthesia Using Multivariable Self-Organizing Fuzzy Logic Structure….Pages 273-295
Interval Type-2 Fuzzy System for ECG Arrhythmic Classification….Pages 297-314
Fuzzy Logic in Evolving in silico Oscillatory Dynamics for Gene Regulatory Networks….Pages 315-327
Back Matter….Pages –