INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 3 ISSUE 1 –JANUARY 2015 - ISSN: 2349 - 9303
A Classification of Cancer Diagnostics based on Microarray Gene Expression Profiling V.S. Gokkul1
Dr. J. Vijay Franklin2
Department of Computer Science & Engineering, Department of Computer Science & Engineering, Bannari Amman Institute of Technology, Bannari Amman Institute of Technology, 1 2 Anna University, Anna University, vijayfranklinj@bitsathy.ac.in gokkul.vs@gmail.com Abstract— Pattern Recognition (PR) plays an important role in field of Bioinformatics. PR is concerned with processing raw measurement data by a computer to arrive at a prediction that can be used to formulate a decision to be taken. The important problem in which pattern recognition are applied have common that they are too complex to model explicitly. Diverse methods of this PR are used to analyze, segment and manage the high dimensional microarray gene data for classification. PR is concerned with the development of systems that learn to solve a given problem using a set of instances, each instances represented by a number of features. The microarray expression technologies are possible to monitor the expression levels of thousands of genes simultaneously. The microarrays generated large amount of data has stimulate the development of various computational methods to different biological processes by gene expression profiling. Microarray Gene Expression Profiling (MGEP) is important in Bioinformatics, it yield various high dimensional data used in various clinical applications like cancer diagnostics and drug designing. In this work a new schema has developed for classification of unknown malignant tumors into known class. According to this work an new classification scheme includes the transformation of very high dimensional microarray data into mahalanobis space before classification. The eligibility of the proposed classification scheme has proved to 10 commonly available cancer gene datasets, this contains both the binary and multiclass data sets. To improve the performance of the classification gene selection method is applied to the datasets as a preprocessing and data extraction step. Index Terms— Pattern Recognition, Microarray Gene Expression Profiling, Mahalanobis, Classifier, Gene Selection —————————— —————————
1INTRODUCTION
Term cancer does not refer to one disease, but rather to many diseases that can occur in various regions of the body. Every type of cancer is characterized to growth of cell. The cancer is third most common diseases and the second leading cause of death in this world. Detection of cancer is the research topic with significant importance. Every gene array techniques have been shown to provide inside into cancer study and the molecular profiling based on gene expression array technology is expected to the promise of precise cancer detection and the classification. The most important problems in the treatment of cancer is the early detection of the disease. If the cancer is detected in later stages, it compromised the function of one or more organ systems and is widespread entire body. Methods for the early detection of cancer are of utmost importance and are an active area of current research. An important step in the diagnosis of cancer is classification of different types of unknown malignant tumors to known classes.
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After the initial detection of a cancerous growth valid diagnosis and staging of disease are essential for the design of treatment plan. Gene chip analysis the Microarray technology is a powerful tool for genomic analysis. It gives a global view of the genome in a single experiment. Data analysis of the microarray is a vital part of the experiment. Each microarray study comprises multiple microarrays, each giving tens of thousands of data points. So the volume of data is bean growing exponentially as microarrays grow larger, the analysis becomes high challenging. During scanning image the image processing techniques is applied. It is any form of signal processing for which the input is an image, such as a photograph or video frame, the output of image processing may be either an of characteristics or parameters related to the input image. After applying image processing techniques, image that is scanned from microarray gene chip is transformed to a data matrix.
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