Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies.

Data mining is usually mentioned in the broader setting of knowledge discovery in databases (KDD), and is viewed as a single step in a larger process called the KDD process. Frequent Pattern Mining (FPM) plays a vital role especially in the real time data mining research because of its wide applicability in industry areas, including process control, production data mining and many other important real time data mining tasks. Creating an association between variables is always of interest in genomic studies. FPM has been applied successfully for discovering interesting association patterns between various genes. Motivated by several heuristics to reduce the number of database scans in the context of frequent pattern mining, the concept of fuzziness on the original gene expression data set was provided in order to discretize the value in terms of under expressed and over expressed genes. Certain soft computing approaches were used to optimize the findings and generate frequent patterns based on the fuzzy frequent pattern mining algorithms. It was observed that fuzzy set helped a lot to find better results in terms of number of frequent patterns.

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification.