Skip to main content
Article thumbnail
Location of Repository

An integrated method for cancer classification and rule extraction from microarray data

By Liang-Tsung Huang

Abstract

Different microarray techniques recently have been successfully used to investigate useful information for cancer diagnosis at the gene expression level due to their ability to measure thousands of gene expression levels in a massively parallel way. One important issue is to improve classification performance of microarray data. However, it would be ideal that influential genes and even interpretable rules can be explored at the same time to offer biological insight

Topics: Research
Publisher: BioMed Central
OAI identifier: oai:pubmedcentral.nih.gov:2653531
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles

    Citations

    1. (2003). A practical approach to microarray data analysis.
    2. (1999). AJ: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA
    3. (1992). An information theoretic approach to rule induction from databases.
    4. (2002). AN: Deriving quantitative conclusions from microarray expression data. Bioinformatics
    5. Chi2: Feature Selection and Discretization of Numeric Attributes.
    6. (2006). Classification of microarray data with factor mixture models. Bioinformatics
    7. (2003). Di Bello C: PCA disjoint models for multiclass cancer analysis using gene expression data. Bioinformatics
    8. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression.
    9. (1999). ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science
    10. (2005). Frank E: Data Mining: Practical machine learning tools and techniques. 2nd edition.
    11. (2008). Furlanello C: Machine learning methods for predictive proteomics. Brief Bioinform
    12. (2001). Gene-expression profiles in hereditary breast cancer.
    13. (2007). Gromiha MM, Ho SY: iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations. Bioinformatics
    14. (2008). Gromiha MM: Analysis and prediction of protein folding rates using quadratic response surface models.
    15. (2003). Hanash S: Mining gene expression databases for association rules. Bioinformatics
    16. (2000). Haussler D: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics
    17. (2004). HW: Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics
    18. (2002). Ito T: Extraction of knowledge on protein-protein interaction by association rule discovery. Bioinformatics
    19. (2006). Knowledge acquisition and development of accurate rules for predicting protein stability changes. Comput Biol Chem
    20. (2006). Koutroumbas K: Pattern recognition 3rd edition.
    21. (2005). Lambert-Lacroix S: Classification using partial least squares with penalized logistic regression. Bioinformatics
    22. (2003). Linear regression and two-class classification with gene expression data. Bioinformatics
    23. (1979). LL: Structured design: fundamentals of a discipline of computer program and systems design. Englewood Cliffs,
    24. (2005). Mewes HW: Gene selection from microarray data for cancer classification – a machine learning approach. Comput Biol Chem
    25. MR: Mining molecular fragments: finding relevant substructures of molecules.
    26. (2006). Pascual-Montano A: Integrated analysis of gene expression by Association Rules Discovery.
    27. (2001). Recursive partitioning for tumor classification with gene expression microarray data. Proc Natl Acad Sci USA
    28. (2007). Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree model.
    29. (2005). Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics
    30. (2003). Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. Bioinformatics
    31. (2002). SJ: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat Genet
    32. (2000). T: Comparison of discrimination methods for the classification of tumors using gene expression data.
    33. (2006). Topology-based cancer classification and related pathway mining using microarray data. Nucleic Acids Res
    34. (2002). Wong L: Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. Bioinformatics

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.