Data Mining for biological data analysis

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Data Mining
for Biological Data Analysis

2. Explosive Growth in
• Genomics.
• Proteomics.
• Functional Genomics. BIOINFOMATICS
• Biomedical Research.
Main :-
Identification and Comparative analysis of the genome
of human and other species to the investigation of
 genetic networks.
 protein pathways.
 development of new pharmaceuticals.
 advance in cancer therapy.

3. Different Cell types

4. DNA
• It form foundation of genetic codes of all living
organism.
• DNA Sequences comprises of 4 basic building
blocks , called NUCLEOTIDES (or bases)
Adenine (A)
Cytosine (C)
Guanine (G)
Thymine (T)

5. DNA Sequences
• Make Effective Presentations
• Using Awesome Backgrounds
• Engage your Audience
• Capture Audience Attention

6. • DNA carry information and biochemical
machinery that can be copied from generation
to generation.
• Process : -
• Insertions
• Deletions different evolution
paths
• Mutations

7. • DNA contains thousands of particular segments
called genes.
• Genome complete set of genes of an organism.
(20,000 – 25,000 genes)
• Genomics is the analysis of genome sequences.
• Proteins – Essential molecule – perform life
functions and make up majority cellular
structures.
• Genes contain “instructions” for making
proteins.
• In order to be executed these “instructions” have
to be transcribed into mRNA.

8. • Proteins are defined by a sequence of amino
acids (20 types).
• Proteome- complete set of protein molecules
present in a cell, tissue, or organism.
• Proteomics is the study of Proteome sequences.
• Genes make up only 2% of the human genome.
• The remainder consisting of non coding regions.

10. Challenges :
Identification DNA or amino
acid sequences patterns.

11. Data Mining
contributions to Biological
Data Analysis in the following
aspects…

12. Semantic integration of heterogeneous,
distributed genomic and proteomic
databases
• (Proteomics and Genomics ) Data produced by
different labs need to be integrated.
• Cross site analysis of biological data from the correct
linkage between them.
• Data mining can be used to perform data cleaning,
integration, object reconciliation to merge
heterogeneous databases.

13. Alignment, indexing, similarity search,
and comparative analysis of multiple
nucleotide/protein sequences.
• Build phylogenetic trees
• Similarity search
• Difference search

14. Discovery of structural patterns and
analysis of genetic networks and
protein pathways.
• 3D structure of proteins heavily affects their
functionalities.
• Prediction of protein structures from its
relative positions and distance between them.
• Discovery of regularities.

15. Association and path analysis : identifying
co-occurring gene sequences and linking
genes to different stages of disease
development.
• Analysis of gene associations in diseases.
• Discovery of sequential patterns of genes correlated
to different stages of diseases.
• Path Analysis - > develop pharmaceutical
interventions on time.

16. Visualization tools in genetic data
analysis.
• Pattern understanding.
• Support to knowledge discovery.
• Interactive data exploration.

18. THANK YOU