FM-GA and CM-GA for gene microarray analysis.
نویسندگان
چکیده
In this paper, we propose two new approaches, FM-GA and CM-GA, to identify significant genes from microarray datasets. FM-GA and CM-GA combine our innovative FM-test and CM-test with genetic algorithm (GA), respectively, and leverage the strengths of GA. The performance of FM-GA and CM-GA was evaluated by the classification accuracy of decision trees constructed with the selected genes. Experiments were conducted to demonstrate the superiority of the proposed method over other approaches.
منابع مشابه
Identification 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 کاملA multiple-filter-GA-SVM method for dimension reduction and classification of DNA-microarray data
The following article proposes a Multiple-Filter by using a genetic algorithm (GA) combined with a support vector machine (SVM) for gene selection and classification of DNA microarray data. The proposed method is designed to select a subset of relevant genes that classify the DNA-microarray data more accurately. First, three traditional statistical methods are used for gene selection. Then diff...
متن کاملP-157: Polymorphic Core Promoter GA-repeats Alter Gene Expression of The Early Embryonic Developmental Genes
Background: We examine the GA-repeat core promoters of MECOM and GABRA3 in human embryonic kidney-293 cell line and show that those GA-repeats have promoter activity,and those different alleles of the repeats can significantly alter gene expression.We propose a novel role for GA-repeat core promoters to regulate gene expression in the genes involved in development and evolution. Materials and M...
متن کاملImproving Prediction Accuracy of Tumor Classification by Re-using the Discarded Genes during Gene Selection
Background: Since the high dimensionality of gene expression microarray data set hurts generalization performance of classifiers, feature selection has been widely used in the bioinformatics field, which selects relevant features and discards irrelevant and redundant features. While redundant features contain useful information, so multi-task learning is a novel technique to improve prediction ...
متن کاملPerformance Analysis of Genetic Algorithm with kNN and SVM for Feature Selection in Tumor Classification
Abstract—Tumor classification is a key area of research in the field of bioinformatics. Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. The main drawback of gene expression data is that it contains thousands of genes and a very few samples. Feature selection methods are used to select the informative genes from the microarray. These methods...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Advances in experimental medicine and biology
دوره 680 شماره
صفحات -
تاریخ انتشار 2010