An understanding towards genetics and epigenetics is essential to cope up with the paradigm shift which is underway. Personalized medicine and gene therapy will confluence the days to come. This review highlights traditional approaches as well as current advancements in the analysis of the gene expression data from cancer perspective. Due to improvements in biometric instrumentation and automation, it has become easier to collect a lot of experimental data in molecular biology. Analysis of such data is extremely important as it leads to knowledge discovery that can be validated by experiments. Previously, the diagnosis of complex genetic diseases has conventionally been done based on the non-molecular characteristics like kind of tumor tissue, pathological characteristics, and clinical phase. The microarray data can be well accounted for high dimensional space and noise. Same were the reasons for ineffective and imprecise results. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data. While differences in efficiency do exist, none of the well-established approaches is uniformly superior to others. The quality of algorithm is important, but is not in itself a guarantee of the quality of a specific data analysis. http://kaashivinfotech.com/ http://inplanttrainingchennai.com/ http://inplanttraining-in-chennai.com/ http://internshipinchennai.in/ http://inplant-training.org/ http://kernelmind.com/ http://inplanttraining-in-chennai.com/ http://inplanttrainingchennai.com/

1. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND
BIOINFORMATICS, VOL. 11, NO. 3, MAY/JUNE 2014
Mining Gene Expression Data Focusing Cancer Therapeutics: A Digest

2. Abstract
 An understanding towards genetics and epigenetics is essential to cope up
with the paradigm shift which is underway. Personalized medicine and gene
therapy will confluence the days to come.
 This review highlights traditional approaches as well as current advancements
in the analysis of the gene expression data from cancer perspective.
 Due to improvements in biometric instrumentation and automation, it has
become easier to collect a lot of experimental data in molecular biology.
 Analysis of such data is extremely important as it leads to knowledge discovery
that can be validated by experiments. Previously, the diagnosis of complex
genetic diseases has conventionally been done based on the non-molecular
characteristics like kind of tumor tissue, pathological characteristics, and
clinical phase.
 The microarray data can be well accounted for high dimensional space and
noise. Same were the reasons for ineffective and imprecise results. Several
machine learning and data mining techniques are presently applied for
identifying cancer using gene expression data.
 While differences in efficiency do exist, none of the well-established
approaches is uniformly superior to others. The quality of algorithm is
important, but is not in itself a guarantee of the quality of a specific data
analysis.

3. EXISTING SYSTEM
 Gene expression is the activation of a gene that results in a
protein which tends to indentify only the Gene
manipulation for Cáncer therapeutics.
 In our existing approac , identification of cancer by the gene
expression have been implemented.
 The Genome of a Differentiated Cell Contains all the Genes
required to find the affected cells using Microarrays to
Investigate the “Expression” of Thousands of Genes at a
Time.
 Splicing
 Polyadenylation
 Stability
 Discretized gene expressions can be used as descriptors of
the specific states of gene for the cancer prediction analysis.

4. PROPOSED SYSTEM
 Predicting Cancer by analyzing gene and converting the
gene expression is the proposed concept of our project,
which leads to identifying and analyzing the cancer result
set .
 Controlling Gene Activity From Gene to Functional Protein
& Phenotype has also been analyzed in order to identify the
cancer cells.
 In our proposed methodology, the experts documental DNA
data methylation(Gene expression segments) is a kind of
binding site for proteins which make DNA inaccessible to be
in alive state.
 Semantic Ontology based Mining Gene Expression analysis
tends to Compare the gene expression values by using the
comparative Knowledge Consolidator.
 Supervised Multi Attribute Clustering Algorithm has been
used to find the Best Rule Classification in the gene
expression to find the Final Prediction of cancer disease

5. SYSTEM ARCHITECTURE
• HARWARE REQUIREMENT:
Processor : Core 2 duo
Speed : 2.2GHZ
RAM : 2GB
Hard Disk : 160GB
• SOFTWARE REQUIREMENT:
Platform : DOTNET (VS2010) , ASP.NET Dotnet
framework 4.0
Database : SQL Server 2008 R2

6. ARCHITECTURE DIAGRAM