SUPERVISED MULTI ATTRIBUTE GENE EXPRESSION DATA FOCUSING ON CANCER THERAPEUTICS

Keywords

Association rules, cancer, clustering, data mining, gene expression data, next generation sequencing.

Abstract

Cancer research - One of the major research areas in the field of medical. Pointed out the exact tumor types provides an optimized solution for better treatment and toxicity minimization due to medicines to the patients. To get a clear picture on the insight of a problem, a clear cancer classification analysis system needs to be pictured followed by a systematic approach to analyze global gene expression which provides an optimized solution for the identified problem area. Molecular diagnostics provides a promising option of systematic human cancer categorization, but these tests are not widely functional because characteristic molecular markers for most solid tumor save yet to be identified. Recently, DNA microarray-based tumor gene expression profiles have been used for cancer diagnosis. Existing system focussed in ranging from old nearest neighbor analysis to support vector machine manipulation for the learning portion of the classification model. We don’t have a clear picture of supervised classifier which can manage knowledge attributes coming two different knowledge streams. The input from multiple source, create an ontological store, cluster the data with attribute match association rule and followed by classification with the knowledge acquired.