A novel filter approach for efficient selection and small round blue-cell tumor cancer detection using microarray gene expression data

2017 ◽  
Author(s):  
Pradeep Singh ◽  
Alok Shukla ◽  
Manu Vardhan
Keyword(s):  
Cancer Detection ◽  
Microarray Gene Expression Data ◽  
Cell Tumor ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Blue Cell ◽  
Microarray Gene ◽  
Filter Approach ◽  
Round Blue Cell Tumor ◽  
Efficient Selection

A Survey on Deep Learning Techniques for Prognosis and Diagnosis of Cancer from Microarray Gene Expression Data

2019 ◽  
Vol 16 (12) ◽  
pp. 5078-5088 ◽  
Author(s):  
Rahul Shahane ◽  
Md. Ismail ◽  
C. S. R. Prabhu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Cancer Detection ◽  
Gene Expression Analysis ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Cancer Subtypes ◽  
Diagnosis Of Cancer ◽  
Microarray Gene

The gene expression classification and identification from DNA microarray data is efficient technique for cancer diagnosis and prognosis for specific cancer subtypes. DNA microarray technology has great potential to discover information from expression levels of thousands of gene. The collection of significant genes which can improve the accuracy can give proper direction in early diagnosis of cancer. Cancer may be of different subtypes. Cancer detection from microarray gene expression data has major challenge of low sample size, high dimensionality and complexity of the data. There is a need for fast and computationally efficient method to deal with these kind of challenges. Deep Learning has succeeded in numerous fields such as image, video, speech, and text processing. Gene expression analysis is a unique challenge to Deep Learning for various cancer detection and prediction tasks in order to set specific biomarkers for different cancer subtypes. In this paper, we briefly discuss the strengths of different Deep Learning architectures for a cancer detection and prediction of various types of cancer through gene expression analysis.

Cox Survival Analysis of Microarray Gene Expression Data Using Correlation Principal Component Regression

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Qiang Zhao ◽  
Jianguo Sun
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Principal Component Regression ◽  
Predictive Ability ◽  
Principal Component ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
New Approach ◽  
Microarray Gene

Statistical analysis of microarray gene expression data has recently attracted a great deal of attention. One problem of interest is to relate genes to survival outcomes of patients with the purpose of building regression models for the prediction of future patients' survival based on their gene expression data. For this, several authors have discussed the use of the proportional hazards or Cox model after reducing the dimension of the gene expression data. This paper presents a new approach to conduct the Cox survival analysis of microarray gene expression data with the focus on models' predictive ability. The method modifies the correlation principal component regression (Sun, 1995) to handle the censoring problem of survival data. The results based on simulated data and a set of publicly available data on diffuse large B-cell lymphoma show that the proposed method works well in terms of models' robustness and predictive ability in comparison with some existing partial least squares approaches. Also, the new approach is simpler and easy to implement.

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