Welcome to Rocky 1

Welcome to Rocky 1

“Go to the Mountains and Get their Good Tidings”

Inspirations:

Adrenaline

Beauty

Life

– John Muir

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

Aristotle 384-322 BC

Linnaeus1707-1778

Darwin1809-1882

Mendel1822-1884

Franklin1920-1958

A timeline and some great minds of biology

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…

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

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)

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

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?

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

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

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?)

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?

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

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.

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