BIOI 7711 Introduction to Bioinformatics - PowerPoint PPT Presentation

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BIOI 7711 Introduction to Bioinformatics

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Beauty. Life. John Muir. Inspiration for a Revolution! ... base, from textbooks, domain experts, journal articles, other databases ... – PowerPoint PPT presentation

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Title: BIOI 7711 Introduction to Bioinformatics


1
Welcome to Rocky 1
2
Go to the Mountains and Get their Good Tidings
John Muir
  • Inspirations
  • Adrenaline
  • Beauty
  • Life

3
Inspiration for a Revolution!
  • Science is in the midst of a tremendous explosion
    of knowledge regarding of life
  • Exponentially growing knowledge challenges
    humanitys ability to integrate and appreciate it
  • Our era cries out for big ideas

4
A timeline and some great minds of biology
5
Why Rosalind Franklin?
  • Womens enormous contributions to the study of
    life have often been downplayed
  • Before she died of cancer at age 37, she produced
    the first X-ray crystal structure of DNA
  • Watson and Crick were shown this image shortly
    before they produced their double-helix model
  • Data drives modeling

6
The challenge of exponentially growing knowledge
  • Numbers are articles from a given year.
  • Fits an exponential curve with a 4.32 growth
    rateR2 0.998

7
Life is deeply connectedHigh order interactions
dominate
  • Unsuspected connections in the last 3 years
  • Uber-oncogene P53 plays an important role in
    aging
  • Expression array studies of remodeling cardiac
    tissue after heart failure implicate role for
    genes well studied in pregnancy and embryological
    development
  • Panadol, a drug developed for cardiovascular
    illness turn out to be very important in the
    treatment of depression.
  • Gene-gene (or protein-protein) interactions are
    not pairwise, but very high order (often gt10)

8
Towards The Biological Knowledge-base
  • Inferential potential of a unified knowledge-base
    transcends human ability
  • Even heroic bioscientists cant keep up with
    flood of information as disciplinary boundaries
    break down.
  • Computational integration efforts
  • SOAP, GRID and especially the Semantic Web
  • Beyond integration
  • Knowledge dissemination timing and
    comprehensibility
  • Making a compelling story from disparate bits of
    evidence

9
Biognostic MachinesAn AI Vision for
Bioinformatics
  • From the Greek??????(life) and????????(knowing)
  • The integration of humanitys knowledge of life
    in a computational system that can interact with
    bioscientists as a knowledgeable colleague
  • Keeps up with the literature
  • Can provide explanations and evidence for its
    statements
  • Transcends disciplinary and terminological
    boundaries
  • AI to the rescue?

10
A bit of AI
  • Cognitive systems are driven by goals
  • Experience, knowledge, memory, practice,
    learning, etc. inform both perception and action
  • Sense perception provides incomplete and
    error-prone information about the world
  • Action is organized and controlled to achieve
    goals (perhaps opportunistically)
  • Mind is many distinct processes working together

11
Biognostic AI
  • Goals
  • Improve human health, diagnose and treat disease
  • Pharmaceuticals their design and improvement
  • Causal generalizations, understanding
  • Experience (knowledge, memory, etc.)
  • Up-to-date fact/knowledge-base, from textbooks,
    domain experts, journal articles, other databases
  • Library of physical, statistical logical models
    and classes of models
  • Sets of models parameters for particular
    applications

12
Biognostic Sensation
  • Sensation is the use of pattern recognition
    (statistics) and knowledge to recognize
    opportunities for achieving goals via perceptions
  • Biognostic Perceptions
  • The biomedical literature (via information
    extraction)
  • Databases GO/A, GenBank, expression databases,
    etc.
  • Sense vocabulary GO, UMLS, NCI common data
    elements, ESV vocabulary, MAGE-ML, etc.
  • Instruments? MS, NMR, etc. (or better from
    databases?)

13
Biognostic Actions Abilities
  • Extract information from the literature
  • Select models, fit parameters from data
  • Learning, optimization, model competition
  • Simulation / Prediction
  • Application of models to unobserved circumstances
  • Creation of new classifications or
    categorizations
  • Communicating
  • Explain, justify, answer questions,
    visualize/diagram
  • Design experiments monitor or control
    instruments?

14
Vision versus Speculation
  • Vision is necessary for engineering the tools to
    achieve it. Speculation is ungrounded and a
    distraction from doing the work
  • Sometimes hard to tell the difference
  • Biognostic machines may be vision, since
  • Many pieces starting to fall into place
    Ontology, information extraction, semantic web,
    etc. etc.
  • We are not alone Paul Allens Project Halo

15
Guide to next few days
  • Purpose of the meeting is to build community
  • Get to know each others names, work,
    institutions
  • Find common interests and potential
    collaborations
  • Let your hair down, have big ideas, have fun!
  • Afternoons are part of the program
  • Informal interactions are just as important as
    talks
  • Good skiers find Elvis Marylin shrines (Back
    of Bell)
  • Novices create a small group (4-8) for a joint
    lesson.
  • Enjoy the town its easily walkable.

16
Thanks!
  • International Society for Computational Biology
    Stephanie Hagstrom
  • CU Center for Computational Biology Stephen
    Billups
  • IBM (for dinner!), Kirk Jordan, Alex Zekulin, and
    the rest
  • Apple and the other sponsors
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