Back to Search
Start Over
Gene selection from microarray data for cancer classification--a machine learning approach
- Source :
- Computational biology and chemistry. 29(1)
- Publication Year :
- 2004
-
Abstract
- A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, naïve Bayes and support vector machines, we show that classification performance at least as good as published results can be obtained on acute leukemia and diffuse large B-cell lymphoma microarray data sets. We also demonstrate that a combined use of different classification and feature selection approaches makes it possible to select relevant genes with high confidence. This is also the first paper which discusses both computational and biological evidence for the involvement of zyxin in leukaemogenesis.
- Subjects :
- Decision tree
Feature selection
Biology
Machine learning
computer.software_genre
Biochemistry
Bayes' theorem
Structural Biology
Artificial Intelligence
Microarray databases
Humans
Glycoproteins
Oligonucleotide Array Sequence Analysis
Microarray analysis techniques
business.industry
Gene Expression Profiling
Organic Chemistry
Precursor Cell Lymphoblastic Leukemia-Lymphoma
Zyxin
Support vector machine
Gene expression profiling
Computational Mathematics
Cytoskeletal Proteins
Leukemia, Myeloid, Acute
Artificial intelligence
business
computer
Algorithms
Curse of dimensionality
Subjects
Details
- ISSN :
- 14769271
- Volume :
- 29
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Computational biology and chemistry
- Accession number :
- edsair.doi.dedup.....4557438e019388417676b586c4628aee