Soft Computing Methodologies for Cluster Analysis and Gene Prediction
Cluster Analysis using SOM and Gene Prediction using BPN and LVQ
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Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. Soft computing covers a wide range of application areas, including optimisation, data analysis and data mining, computer graphics and vision, prediction and diagnosis, design, intelligent control, and traffic and transportation systems. Bioinformatics is the application of computer technology to the management and manipulation of biological information. Soft computing offers a promising approach to achieve efficient and reliable heuristic solution for ...
Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost. Soft computing covers a wide range of application areas, including optimisation, data analysis and data mining, computer graphics and vision, prediction and diagnosis, design, intelligent control, and traffic and transportation systems. Bioinformatics is the application of computer technology to the management and manipulation of biological information. Soft computing offers a promising approach to achieve efficient and reliable heuristic solution for the bioinformatics problem areas such as clustering, pattern recognition and prediction related domains. In this book, soft computing methodologies for cluster analysis of microarray data using Kohonen's Self Organising Maps and prediction of genes using Back Propagation Network and Learning Vector Quantization Network are discussed.
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