Your SlideShare is downloading. ×
The Importance Of Data Mining By Musa Mohd. Nordin, Noor
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

The Importance Of Data Mining By Musa Mohd. Nordin, Noor

4,542
views

Published on

Noor Conference | Global Knowledge Forum | http://www.noor.org.sa | Day 2 - Panel 2 - The Importance Of Data Mining By Musa Mohd. Nordin, Noor

Noor Conference | Global Knowledge Forum | http://www.noor.org.sa | Day 2 - Panel 2 - The Importance Of Data Mining By Musa Mohd. Nordin, Noor

Published in: Education, Technology

1 Comment
3 Likes
Statistics
Notes
  • Fioricet is often prescribed for tension headaches caused by contractions of the muscles in the neck and shoulder area. Buy now from http://www.fioricetsupply.com and make a deal for you.
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
No Downloads
Views
Total Views
4,542
On Slideshare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
0
Comments
1
Likes
3
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Transcript

    • 1. THE IMPORTANCE OF DATA MINING Musa Mohd. Nordin FRCP, FAMM President Federation of Islamic Medical Associations
    • 2. WHAT WE WILL DISCUSS :
      • Why data mine ?
      • What is data mining ?
      • Applications of data mining.
      • Data mining vis a vis genomics & informatics
      • Conclusions
    • 3.
      • Lots of data collected and warehoused
        • Web data, e-commerce
        • Purchases at department
        • stores
        • Bank & Credit Card transactions
      • Computers cheaper & more powerful
      • Competitive pressure is strong
        • Make better decisions
        • Serve customers
        • Gain competitive edge
      Why Mine Data?
    • 4. Why Mine Data? Scientific Viewpoint
      • Data collected & stored at enormous speeds (GB/hour)
        • remote sensors on a satellite
        • telescopes scanning the skies
        • microarrays generating gene expression data
        • scientific simulations generating terabytes of data
      • Data volumes overwhelm traditional techniques
      • - enormity, multi dimensional, heterogenous data
      • Data mining may help scientists
        • in segmenting & analysing data
        • in hypothesis generation
        • in knowledge discovery
    • 5. SIZE OF MEDICAL KNOWLEDGE
      • NLM Meta Thesaurus
      • - 875,255 concepts
      • - 2.14 million concepts names
      • Biomarkers & prognosis
      • - one marker in 12 years (1989-2001)
      • - 400 markers in 1 year (2004)
      • Drug development
      • - one molecule & 1,000 compound (1985)
      • - 40,000 cDNAs and 1 million compounds (2005)
    • 6. Mining Large Data Sets - Motivation
      • Information “hidden” in the data that is not readily evident
      • Human analysts may take weeks to discover useful information
      • Much of the data is never analyzed at all
      The Data Gap Total new disk (TB) since 1995 Number of analysts From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”
    • 7.  
    • 8.  
    • 9.  
    • 10.  
    • 11. Data mining is the process of identifying VALID, NOVEL, potentially USEFUL & UNDERSTANDABLE patterns in data.
    • 12.
      • Emerged late 1980s
      • Flourished 1990s
      • Roots traced to 3 disciplines :
      • - Classical Statistics
      • - Artificial Intelligence
      • - Machine Learning
      • Pre 1993 : “Torturing
      • the data into a confession”
      • Post 1993 : “Charming
      • the data into a confession”
      Origins of Data Mining Machine Learning/ Pattern Recognition Statistics/ AI Data Mining Database systems
    • 13. Transformed Data Target Data RawData Knowledge Data Mining Transformation Interpretation & Evaluation Selection & Cleaning Integration Understanding Knowledge Discovery Process DATA Ware house Knowledge __ ____ __ ____ __ ____ Patterns and Rules
    • 14. DATA MINING – MEDICAL APPLICATIONS
      • Medical diagnostics tools
      • Medical image analysis
      • Micro-array gene expression
      • Protein structure & fxn prediction
      • New drug development
      • Disease surveillance
      • Bioterrorism surveillance
      • Environmental health impacts
    • 15. Strong Government Initiatives US: US$3B for Human Genome Project Germany: US$62M & US$18M to support proteomics and bacterial genomes over 3 years respectively Britain: Budget to grow at 7% a year for next 4 years in bioinformatics and other post-genomics research Italy: US$195M fund to focus on human genetics, cancer and bioinformatics Sweden: US$91.4M for biotech, biosciences, healthcare Singapore: US$1.2B in life sciences Malaysia: BioValley will be valued at US$13.2B in 10 years Japan: US$489.6M invested towards sequencing and analysis Korea: US$1.7M for 2 plant genome projects
    • 16.  
    • 17.
      • There is 6 feet of DNA in each of our cells packed into a structure only 0.0004 inches across
      • There are 100 trillion (100,000,000,000,000) cells in the body
      • If all the DNA in the human body was put end to end it would reach to the sun and back over 600 times (100 trillion x 6 feet divided by 93 million miles = 1200).
    • 18. Life sciences research: from gene to function Gene NH 2 COOH Protein Genome-wide micro-array analysis “ High-throughput” protein-analysis mRNA AAAAAAAAA function-2 function-1 function-n Whole-genome sequence projects Protein function: -prediction by bioinformatics -proof by laboratory research cell nucleus Gene expression by RNA synthesis mRNA translation by protein synthesis DNA
    • 19. Paradigm Shift in Life Sciences
      • Past experiments were hypothesis driven
        • Evaluate hypothesis
        • Complement existing knowledge
      • Present experiments are data driven
        • Discover knowledge from large amounts of data
    • 20. DATA MINING : GENOMICS & BIOINFORMATICS
      • Experiments increasingly complex
      • Driven by increase of detector developments
      • Results in an increase in amount and complexity of data
      • DM to harness this development
      • To translate data into useful biological, medical, pharmaceutical & agricultural knowledge
    • 21. DATA MINING : CONCLUSIONS
      • Knowledge discovery from databases
      • “ Cutting edge” of the art & science of medicine
      • “ Competitive edge” of the business of medicine
      • Applicable to other sciences and arts
      • Knowledge discovery :
      • - in search of excellence (IHSAN)
      • - transformation (ISLAH) towards benefiting humanity
    • 22. Thank You

    ×