Methods of Microarray Data Analysis Papers from CAMDA ’00

Methods of Microarray Data Analysis Papers from CAMDA ’00 / edited by Simon M. Lin, Kimberly F. Johnson.

Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Lin, Simon M. (Editor), Johnson, Kimberly F. (Editor)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 2002.
Edition:1st ed. 2002.
Series:Springer eBook Collection.
Subjects:
Biochemistry.
Human genetics.
Statistics .
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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