Regular Expression Based Pattern Matching for Gene Expression Data to Identify the Abnormality Gnome

被引：3
作者：
Sharmila, L. [1 ]
Sakthi, U. [2 ]
Geethanjali, A. [3 ]
Sagadevan, Suresh [4 ]
机构：
[1] Sathyabama Univ, Dept Comp Sci & Engn, Fac CSE, Madras, Tamil Nadu, India
[2] St Josephs Inst Technol, Dept Comp Sci & Engn, Madras, Tamil Nadu, India
[3] Alpha Coll Engn, Dept Comp Sci & Engn, Madras, Tamil Nadu, India
[4] AMET Univ, Dept Phys, Madras, Tamil Nadu, India
来源：
2017 SECOND INTERNATIONAL CONFERENCE ON RECENT TRENDS AND CHALLENGES IN COMPUTATIONAL MODELS (ICRTCCM) | 2017年
关键词：
Pattern Matching; Regular Expression; Gene Expression Data; Clustering; Data Mining; MOUSE-BRAIN; IDENTIFICATION; ATLAS; MODEL;
D O I：
10.1109/ICRTCCM.2017.71
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The main idea of this paper is to detect and extract an input pattern from a gene expression dataset. Human behavior and health conditions can be identified and classified through their genomic data in accurate manner. In this paper it is motivated to search and identify the abnormal pattern availability in a gene expression data. To do this a Regular Expression based Pattern Matching (REPM) method is proposed for detecting, identifying and counting number of abnormal pattern occurrences in a given dataset. This approach is experimented in MATLAB software the results verified to check the efficiency of REPM method.
引用
收藏
页码：301 / 305
页数：5
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