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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.
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Misfit (Paperback)
Shruti Mishra
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R398
R329
Discovery Miles 3 290
Save R69 (17%)
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Misfit (Hardcover)
Shruti Mishra
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R733
R602
Discovery Miles 6 020
Save R131 (18%)
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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.
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