Papers from CAMDA '00
Buch, Englisch, 189 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 521 g
ISBN: 978-0-7923-7564-7
Verlag: Springer US
Currently, there are no standard procedures for the design and analysis of microarray experiments. focuses on two well-known data sets, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Vorklinische Medizin: Grundlagenfächer Humangenetik
- Naturwissenschaften Biowissenschaften Biochemie (nichtmedizinisch)
- Naturwissenschaften Biowissenschaften Molekularbiologie
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
- Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)
Weitere Infos & Material
Reviews and Tutorials.- Data Mining and Machine Learning Methods for Microarray Analysis.- Evolutionary Computation in Microarray Data Analysis.- Best Presentation — CAMDA ’00.- Using Non-Parametric Methods in the Context of Multiple Testing to Determine Differentially Expressed Genes.- Quality Analysis and Data Normalization of Spotted Arrays.- Iterative Linear Regresssion by Sector.- Feature Selection, Dimension Reduction, and Discriminative Analysis.- A Method to Improve Detection of Disease Using Selectively Expressed Genes in Microarray Data.- Computational Analysis of Leukemia Microarray Expression Data Using the GA/KNN Method.- Classical Statistical Approaches to Molecular Classification of Cancer from Gene Expression Profiling.- Classification of Acute Leukemia Based on DNA Microarray Gene Expressions Using Partial Least Squares.- Applying Classification Separability Analysis to Microarray Data.- How Many Genes Are Needed for a Discriminant Microarray Data Analysis.- Machine Learning Techniques.- Comparing Symbolic and Subsymbolic Machine Learning Approaches to Classification of Cancer and Gene Identification.- Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis.