Data Mining and Applications in Genomics

• Κύριος συγγραφέας: Ao, Sio-Iong (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Dordrecht : Springer Netherlands, 2008.
• Σειρά: Lecture Notes in Electrical Engineering, 25
• Θέματα: Computer science.; Human genetics.; Data mining.; Bioinformatics.; Electronics.; Microelectronics.; Computer Science.; Data Mining and Knowledge Discovery.; Human Genetics.; Computational Biology/Bioinformatics.; Electronics and Microelectronics, Instrumentation.

 Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Topics covered include Genomic Techniques, Single Nucleotide Polymorphisms, Disease Studies, HapMap Project, Haplotypes, Tag-SNP Selection, Linkage Disequilibrium Map, Gene Regulatory Networks, Dimension Reduction, Feature Selection, Feature Extraction, Principal Component Analysis, Independent Component Analysis, Machine Learning Algorithms, Hybrid Intelligent Techniques, Clustering Algorithms, Graph Algorithms, Numerical Optimization Algorithms, Data Mining Software Comparison, Medical Case Studies, Bioinformatics Projects, and Medical Applications. Data Mining and Applications in Genomics offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serve as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics. 

 Data Mining Algorithms -- Advances in Genomic Experiment Techniques -- Case Study I: Hierarchical Clustering and Graph Algorithms for Tag-SNP Selection -- Case Study II: Constrained Unidimensional Scaling for Linkage Disequilibrium Maps -- Case Study III: Hybrid PCA-NN Algorithms for Continuous Microarray Time Series -- Discussions and Future Data Mining Projects.