Feature Selection and Extraction Framework for DNA Methylation in Cancer
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 7
Abstract
Feature selection methods for cancer classification are aimed to overcome the high dimensionality of the biomedical data which is a challenging task. Most of the feature selection methods based on DNA methylation are time consuming during testing phase to identify the best pertinent features subset that are relevant to accurate prediction. However, the hybridization between feature selection and extraction methods will bring a method that is far fast than only feature selection method. This paper proposes a framework based on both novel feature selection methods that employ statistical variation, standard deviation and entropy, along with extraction methods to predict cancer using three new features, namely, Hypomethylation, Midmethylation and Hypermethylation. These new features represent the average methylation density of the corresponding three regions. The three features are extracted from the selected features based on the analysis of the methylation behavior. The effectiveness of the proposed framework is evaluated by the breast cancer classification accuracy. The results give 98.85% accuracy using only three features out of 485,577 features. This result proves the capability of the proposed approach for breast cancer diagnosis and confirms that feature selection and extraction methods are critical for practical implementation.
Authors and Affiliations
Abeer A. Raweh, Mohammad Nassef, Amr Badr
Keywords
Related Articles
- EP ID EP259990
- DOI 10.14569/IJACSA.2017.080705
- Views 81
- Downloads 0
A Survey on Tor Encrypted Traffic Monitoring
Tor (The Onion Router) is an anonymity tool that is widely used worldwide. Tor protect its user privacy against surveillance and censorship using strong encryption and obfuscation techniques which makes it extremely diff...
SentiNeural: A Depression Clustering Technique for Egyptian Women Sentiments
Online Sentiments Analysis is a trending research domain of study which is based on natural language processing, artificial intelligence, and computational linguistics. Negation sentiments usually are not included in se...
Enhanced Random Early Detection using Responsive Congestion Indicators
Random Early Detection (RED) is an Active Queue Management (AQM) method proposed in the early 1990s to reduce the effects of network congestion on the router buffer. Although various AQM methods have extended RED to enha...
The Implementation of Computer based Test on BYOD and Cloud Computing Environment
Computer-based test promises several benefits such as automatic grading, assessment features, and paper efficiency. However, besides the benefits, the organization should prepare the enough infrastructure, network connec...
An Improved Grunwald-Letnikov Fractional Differential Mask for Image Texture Enhancement
Texture plays an important role in identification of objects or regions of interest in an image. In order to enhance this textural information and overcome the limitations of the classical derivative operators a tw...