Using the GEMS System for Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data
نویسندگان
چکیده
We will demonstrate the GEMS system for automated development and evaluation of high-quality cancer diagnostic models and biomarker discovery from microarray gene expression data. The development of GEMS was informed by the results of an extensive algorithmic evaluation using 11 microarray datasets. The system was further evaluated in two cross-dataset applications and using 5 microarray datasets. The performance of models produced by GEMS is comparable or better than the results obtained by human analysts, and these models generalize well to independent samples in cross-dataset applications. The system is freely available for download from http://www.gems-system.org for non-
منابع مشابه
Classification 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 کاملGEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data
The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, we have built a system called GEMS (gene expression model selector) for the automated development and evaluation of high-quality cancer diagnostic models and biomarker discovery from microarray gene expression data. In order to determine and equip the system with the best performing diag...
متن کاملGene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method
Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expressio...
متن کاملDiagnosis of Breast Cancer Subtypes using the Selection of Effective Genes from Microarray Data
Introduction: Early diagnosis of breast cancer and the identification of effective genes are important issues in the treatment and survival of the patients. Gene expression data obtained using DNA microarray in combination with machine learning algorithms can provide new and intelligent methods for diagnosis of breast cancer. Methods: Data on the expression of 9216 genes from 84 patients across...
متن کاملPrediction of blood cancer using leukemia gene expression data and sparsity-based gene selection methods
Background: DNA microarray is a useful technology that simultaneously assesses the expression of thousands of genes. It can be utilized for the detection of cancer types and cancer biomarkers. This study aimed to predict blood cancer using leukemia gene expression data and a robust ℓ2,p-norm sparsity-based gene selection method. Materials and Methods: In this descriptive study, the microarray ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005