1. [DNA chip data mining].
- Author
-
Lee HS
- Subjects
- Algorithms, Cluster Analysis, Computational Biology, DNA analysis, Data Interpretation, Statistical, Genes, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Lymphoma, B-Cell genetics, Lymphoma, B-Cell pathology, Mathematical Computing, Models, Genetic, Multivariate Analysis, Oligonucleotide Array Sequence Analysis methods, Risk Factors, Signal Processing, Computer-Assisted, Survival, Gene Expression Profiling methods
- Abstract
DNA chip data routinely contain gene expression levels of thousands of genes and the analysis should be supported by various computational tools. To be brief, the analysis procedure consists of four steps including image scanning, image processing, mathematical interpretation and biological interpretation. In image processing step, we should detect the spots and measure the signals of the spots and the background. In mathematical interpretation step, first of all we should massage the measured signals to make them appropriate for further mathematical analysis. The massaged data could be analyzed by various computational methods especially when the data were generated for multiple samples comparisons. The clustering techniques including hierarchical clustering, k-means clustering, SOTA, SOM are the most popular methods in this step. Various other multivariate statistics and related machine learning techniques are being introduced and applied to DNA chip data analysis recently. And finally the most important step we should tackle is the biological interpretation task. Although the depth of the domain knowledge about the biological situation under which the data were generated is the most important factor to elucidate the biological context, it could be supported by various bioinformatics tools including MEDLINE abstract processing by NLP techniques or genetic network models constructed by Boolean networks algorithms.
- Published
- 2001