Recursive partitioning for tumor classification with gene expression microarray data

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Recursive partitioning for tumor classification with gene expression microarray data.

Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology based on classification trees and demonstrate...

متن کامل

Gene Selection for Tumor Classification Using Microarray Gene Expression Data

In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computational intelligent techniques for classification accuracy on Leukemia, Lymphoma and Prostate cancer datasets of broad institute and Colon cancer dataset from Princeton gene expression project. This paper also describes re...

متن کامل

Feature Selection in Tumor Classification Using Microarray Gene Expression Data

Feature selection is the process of choosing a subset of the original predictive variables through the elimination of redundant and uninformative representatives. An example of importance is the analysis of gene expression data from DNA microarray hybridization experiments. The data obtained from the experiments usually contain a few samples each with expression levels of a large number of gene...

متن کامل

Optimization Based Tumor Classification from Microarray Gene Expression Data

BACKGROUND An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally,...

متن کامل

Dimension reduction for classification with gene expression microarray data.

An important application of gene expression microarray data is classification of biological samples or prediction of clinical and other outcomes. One necessary part of multivariate statistical analysis in such applications is dimension reduction. This paper provides a comparison study of three dimension reduction techniques, namely partial least squares (PLS), sliced inverse regression (SIR) an...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Proceedings of the National Academy of Sciences

سال: 2001

ISSN: 0027-8424,1091-6490

DOI: 10.1073/pnas.111153698