featureSelectionDNAMethyCancerClassification_01bioinfo

featureSelectionDNAMethyCancerClassification_01bioinfo -...

Info icon This preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
BIOINFORMATICS Vol. 17 Suppl. 1 2001 Pages S157–S164 Feature selection for DNA methylation based cancer classification Fabian Model, P ´ eter Adorj ´ an, Alexander Olek and Christian Piepenbrock Epigenomics AG, Kastanienallee 24, D-10435 Berlin, Germany Received on February 6, 2001; revised and accepted on April 2, 2001 ABSTRACT Molecular portraits, such as mRNA expression or DNA methylation patterns, have been shown to be strongly correlated with phenotypical parameters. These molecular patterns can be revealed routinely on a genomic scale. However, class prediction based on these patterns is an under-determined problem, due to the extreme high dimensionality of the data compared to the usually small number of available samples. This makes a reduction of the data dimensionality necessary. Here we demonstrate how phenotypic classes can be predicted by combining feature selection and discriminant analysis. By comparing several feature selection methods we show that the right dimension reduction strategy is of crucial importance for the classification performance. The techniques are demonstrated by methylation pattern based discrimination between acute lymphoblastic leukemia and acute myeloid leukemia. Contact: [email protected] INTRODUCTION In recent years there has been a large interest in the anal- ysis of mRNA expression by using microarrays (Lockhart & Winzeler, 2000). This technology allows to look at thou- sands of genes, see how they are expressed as proteins and gain insight into cellular processes. An important and sci- entifically interesting application of this technology is the classification of tissue types, especially the prediction of tumor classes (Golub et al. , 1999; Ben-Dor et al. , 2001; Weston et al. , 2001). However, there are some practical problems with the large scale analysis of mRNA based microarrays. They are primarily impeded by the instability of mRNA (Emmert- Buck et al. , 2000). Also sample preparation is complicated by the fact that expression changes occur within minutes following certain triggers. The inability to resolve the in- dividual contributions of such influences on an expression profile, and difficulties with quantifying the gradual nature of the occurring changes complicates data analysis. An alternative approach is to look at DNA methylation (Adorj´an et al. , 2001). Methylation is a modification of cytosine, which occurs either with or without a methyl group attached. This methylation of cytosine can only ap- pear together with guanine as CpG. The methylated CpG can be seen as a 5th base and is one of the major fac- tors responsible for expression regulation (Robertson & Wolffe, 2000). Here we demonstrate that cancer classifica- tion based solely on DNA methylation analysis is possible and that results comparable to mRNA expression can be achieved.
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern