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Biognostic Machines: Revolutionizing Biological Knowledge with AI

Discover how biognostic machines, powered by artificial intelligence, are transforming the field of biology by integrating and disseminating exponentially growing knowledge. Explore the contributions of great minds in biology like Linnaeus, Aristotle, Mendel, Franklin, and Darwin, and learn why Rosalind Franklin's work is significant. Join us to discuss the potential of these machines in advancing human health, pharmaceutical design, and causal generalizations. This meeting aims to build a community of scientists and foster collaborations in this exciting field.

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Biognostic Machines: Revolutionizing Biological Knowledge with AI

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Presentation Transcript

  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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