1. MICROARRAY CLASSIFICATION
USING COMPUTATIONAL
I N T E L L I G E N T T E C H N I Q U ES
By
Smitarani Satpathy
(Roll no:- 312VMIT11025)
Guided by Prof. SWATI VIPSITA
2. Introduction
Bioinformatics can be defined as the application of computer technology to
the management of biological information, encompassing a study of the
inherent genetic information, molecular structure, resulting biochemical
functions and the exhibited phenotypic properties.
The data mining techniques are effectively used to extract meaningful
relationships from
these data.
The application of data mining techniques in Bioinformatics are effectively
used to extract meaningful relationships from these data.
Biological data mining is an emerging field of research and development
posing challenges and providing possibilities in this direction.
3. Continued
The major pattern recognition in data mining task considered here
are clustering, classification , feature selection and rule
generation.
The broad areas of Bioinformatics are Genomic sequence, protein
structure, gene expression micro arrays and gene regulatory
networks
4. Related Work
Cancer gene can find from microarray data by using supervised
machine learning algorithms.
Also abnormal chromosome data can read by this process.
Tumor can diagnosis from the microarray data by some
intelligent techniques.
5. WHY TO USE INTELLIGENT
TECHNIQUES like NN, GA, FL, PSO?
INTELLIGENT techniques possess the real challenge to handle huge
amount of biological data as these data mostly contain noisy
samples.
Since the work entails processing huge amounts of incomplete or
ambiguous biological data, the Neural network, Fuzzy
logic, Roughset etc, concept can be used for handling
.
uncertainty, ambiguity etc
6. MOTIVATION…
WHY TO GO FOR MICROARRAY
CLASSIFICATION???...
The most appealing feature is that information about the
sequence of DNA is not required to construct and use the
microarrays (basically matching and manipulating when the
input sequence is large is a cumbersome task!!)
WHY TO USE INTELLIGENT TECHNIQUES
like NN, GA, FL, PSO?
INTELLIGENT techniques possess the real challenge to handle
huge amount of biological data as these data mostly contain
noisy samples.
7. OBJECTIVE
The main objective of the work will be to implement neural
networks, GA, hybrid neuro-genetic techniques, to maximize
the performance accuracy of the classifier.
Correct classification is of great concern to Biologists and
Researchers as correct drug need to be discovered for the
treatment of patients.
8. MICROARRAY ?
Definition
A microarray is a multiplex lab-on-a-chip.
It is a 2-D array on a solid substrate (usually a glass slide or
silicon thin film- cell) that assays large amount of biological
material using high throughput screening methods.
9. TYPES OF MICROARRAY
DNA Microarray
Protein Microarray
Peptide Microarray
Tissue Microarray
Cellular Microarray
Chemical compound Microarray
Antibody Microarray
Carbohydrate Microarray
10. MICROARRAY CLASSIFICATION
Initially, interest focused on genes co-expressing across sets of
experimental conditions, implying essentially the use of
CLUSTERING techniques.
Recently, focus is on finding genes differentially expressed
among distinct classes of experiments or correlated to diverse
clinical outcomes as well as building predictions.
11. Basic Concept of Gene Expression
Same set of genes in cells in all tissues of an organism.
Central dogma: Gene -> mRNA -> Protein
Why do tissues function differently?
Activity of genes varies over
Tissues
External stimuli and perturbation
Gene expression is the measure of gene activity.
GENE EXPRESSION= #mRNA
13. Application Areas:
SNP Detection.(A single-nucleotide polymorphism
(SNP, pronounced snip) is a DNA sequence variation
occurring when a single nucleotide)
Evolution and Ecological Genomics.
Drug Discovery and Development.
Gene Expression.
Tumor Classification.
14. CONCLUSION
It can finally be concluded that microarray technology possesses
the real challenge for implementation in the medical field of
disease diagnosis due to the following advantages:
Microarrays are useful in detecting smaller changes than routine
karyotypes in case of chromosomal abnormalities..
Maximum Speed (there can be as many as 150 copies of an array
of 12000 genes printed in only one day).
Relatively cheap to use.
User friendly(Neither radioactive nor toxic).
Adaptable and Comprehensive.
Study of many genes simultaneously.
Can measure the expression level of thousands of genes in a single
experiment.
15. REFERENCES
1. Sushmita Mitra and Yoichi Hayashi, “Bioinformatics with
Soft Computing”, IEEE Transaction on Systems, Man and
Cybernetics, vol. 36(5), pp. 616-635, 2006.
2. Zhenqiu Liu, Dechang Chen, and Halima Bensmail, “ Gene
expression data classification using Kernel PCA”, Jr. of
Biomedicine and Biotechnology, vol. 5(2), pp.155-
159, 2005.
3. Caio Soares, Lacey Montgomery, Kenneth Rouse and Juan E.
Gilbert, “Automating Microarray Classification using General
Regression Neural Networks”, 2008 Seventh International
Conference on Machine Learning and Applications, pp. 508-
513, 2008.