A Novel Gene Analysis Method for Biomarker Mining in DNA Microarray Data
نویسندگان
چکیده
Preventing, diagnosing, and treating disease is greatly facilitated by the availability of biomarkers. Recent improvements in bioinformatics technology have facilitated largescale screening of DNA arrays for candidate biomarkers. Here we discuss a gene analysis method that we call the LEAve-oneout Forward selection method (LEAF) for discovering informative genes embedded in expression data, and propose an additional algorithm for extending LEAF’s capabilities. An iterative forward selection method incorporating the concept of leaveone-out cross validation (LOOCV), LEAF provides a discrimination power score (DPS) for genes. We show that LEAF identifies genes that correspond to known biomarkers. Therefore, our method should provide a useful bioinformatics tool for biomedical, clinical, and pharmaceutical researchers.
منابع مشابه
Integration and Reduction of Microarray Gene Expressions Using an Information Theory Approach
The DNA microarray is an important technique that allows researchers to analyze many gene expression data in parallel. Although the data can be more significant if they come out of separate experiments, one of the most challenging phases in the microarray context is the integration of separate expression level datasets that have gathered through different techniques. In this paper, we prese...
متن کاملIdentification of Alzheimer disease-relevant genes using a novel hybrid method
Identifying genes underlying complex diseases/traits that generally involve multiple etiological mechanisms and contributing genes is difficult. Although microarray technology has enabled researchers to investigate gene expression changes, but identifying pathobiologically relevant genes remains a challenge. To address this challenge, we apply a new method for selecting the disease-relevant gen...
متن کاملFeature Selection and Classification of Microarray Gene Expression Data of Ovarian Carcinoma Patients using Weighted Voting Support Vector Machine
We can reach by DNA microarray gene expression to such wealth of information with thousands of variables (genes). Analysis of this information can show genetic reasons of disease and tumor differences. In this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...
متن کاملClassification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest
Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and smal...
متن کاملبه کارگیری خوشهبندی دوبعدی با روش «زیرماتریسهای با میانگین- درایههای بزرگ» در دادههای بیان ژنی حاصل از ریزآرایههای DNA
Background and Objective: In recent years, DNA microarray technology has become a central tool in genomic research. Using this technology, which made it possible to simultaneously analyze expression levels for thousands of genes under different conditions, massive amounts of information will be obtained. While traditional clustering methods, such as hierarchical and K-means clustering have been...
متن کامل