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Welcome to Rocky 1

Welcome to Rocky 1. “Go to the Mountains and Get their Good Tidings”. – John Muir. Inspirations: Adrenaline Beauty Life. Inspiration for a Revolution!. Science is in the midst of a tremendous explosion of knowledge regarding of life

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Welcome to Rocky 1

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  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 humanity’s ability to integrate and appreciate it • Our era cries out for big ideas

  4. Linnaeus1707-1778 Aristotle 384-322 BC Mendel1822-1884 Franklin1920-1958 Darwin1809-1882 A timeline and some great minds of biology

  5. Why Rosalind Franklin? • Women’s 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. Numbers are articles from a given year. Fits an exponential curve with a 4.32% growth rateR2 = 0.998 The challenge of exponentially growing knowledge

  7. Life is deeply connected:High 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 >10)

  8. Towards The Biological Knowledge-base • Inferential potential of a unified knowledge-base transcends human ability • Even heroic bioscientists can’t 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 Machines:An AI Vision for Bioinformatics • From the Greek(life) and(knowing) • The integration of humanity’s 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 Allen’s Project Halo

  15. Guide to next few days • Purpose of the meeting is to build community • Get to know each other’s 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: it’s 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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