A%20knowledge%20based%20approach%20for%20representing,%20reasoning%20and%20hypothesizing%20about%20biochemical%20networks

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A knowledge based approach for representing,
reasoning and hypothesizing about biochemical
networks

• Chitta Baral
• Arizona State University

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Three parts to the talk

• Prediction, Explanation and Planning with respect
  to biochemical networks
• Hypothesis Generation with respect to biochemical
  networks
• Collaborative BioCuration CBioC

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Motivation purpose of interaction databases?

• Suppose We have an almost exhaustive database of
  the intracellular interactions (protein-protein,
  metabolic, etc.) of particular cells.
• What next?
• How will we use this database?
• What if our knowledge is incomplete?

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Motivation Uses of networks pathways

• Visualize the pathways
• Analyze the graphs of the networks
• Compare graphs of the networks
• Use pathway data in conjunction with micro-array
  data analysis
• Do system level simulation
• Is that all?

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Motivation ultimate uses!

• Prediction/System Simulation (Systems Biology?)
• Impact of particular perturbations (say caused by
  a drug that introduces certain proteins to the
  cell membrane or into the cell)
• Do the perturbations have the desired impact?
• Do they mess up something else? (side effects!)
• But thats not all!

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Motivation Explaining observations

• A phenotypical observation (leading to) OR
• an observation that a particular protein or
  chemical has abnormally high concentration
• What is wrong? What is out of the ordinary?
• The cause/explanation will give us approaches to
  fix the problem.
• How deep should the explanations go?
• How do we compare explanations?

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Motivation Designing drugs therapies

• What perturbations (when and where) need to be
  made so as to make the cell behave in a
  particular way?
• In case of cancer prevent proliferation, induce
  apoptosis, prevent migration, etc.

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What if knowledge is incomplete?

• What kind of useful reasoning can we do with
  incomplete knowledge?
• Drug makers dont wait till full knowledge is
  available.
• Answer hypothesis formation

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Motivation Use summary

• The ultimate uses of signaling (metabolic, etc.)
  interaction databases are to do
• Prediction therapy verification determining
  side effects.
• Explanation -- diagnosing what is wrong.
• Planning therapy and drug design.
• Intermediate or immediate use
• Generate Hypothesis

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Initial goal of our research

• Use knowledge representation and reasoning
  techniques to
• Represent interactions
• Reason about these interactions prediction,
  explanation, planning and hypothesis formation.

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Some questions

• Isnt it a little premature?
• We know very little about the networks
• New knowledge is being constantly added
• Why knowledge representation and reasoning?
• Why not simulation
• Why not use Petri nets, p-calculus
• Why a knowledge-based approach? Why not a data
  base approach? Whats the difference?

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Our approach present and future

• Yes, prediction is kind-of same as simulation
• Incompleteness of information is an issue though!
• But hard to do explanation generation, or design
  of therapies (planning) using simulation
  guesses can be verified using simulation though
• The core database query languages can not express
  explanation or planning queries.
• Dealing with incompleteness!

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Dealing with incompleteness ongoing and future
work

• Is one of the key criteria behind a good
  knowledge representation language when building
  AI systems.
• Need to be non-monotonic.
• Need to be elaboration tolerant.
• Proper analysis leads to hypothesizing
• If certain observations can not be satisfactorily
  explained by the existing knowledge about the
  network then use general biological knowledge to
  hypothesize

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Motivation -- summary

• Goal To emulate the abstract reasoning done by
  biologists, medical researchers, and pharmacology
  researchers.
• Types of reasoning prediction, explanation,
  planning and hypothesis formation.
• Current system biology approaches mostly
  prediction.
• Ongoing issues Dealing with incomplete knowledge
  and elaboration tolerance.

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Related Works

• Quantitative approaches. (hybrid systems, use of
  differential equations)
• Graphical representations.
• Other qualitative approaches.
• Petri Nets
• ?-calculus
• Pathway Logic
• Model Checking

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Overview of our approach

• Represent signal network as a knowledge base that
  describes
• actions/events (biological interactions,
  processes).
• effect of these actions/events.
• triggering conditions of the actions/events.
• To query using the knowledge base
• Prediction explanation planning Hypothesis
  generation
• BioSigNet-RR (Biological Signal Network -
  Representation and Reasoning) and BioSigNet-RRH
  systems.

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Foundation behind our approach

• Research on representing and reasoning about
  dynamic systems (space shuttles, mobile robots,
  software agents)
• causal relations between properties of the world
• effects of actions (when can they be executed)
• goal specification
• action-plans
• Research on knowledge representation, reasoning
  and declarative problem solving the AnsProlog
  language.

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An NFkB signaling pathway
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An NFkB signaling pathway
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Syntax by example

• bind(TNF-a,TNFR1) causes trimerized(TNFR1)
• trimerized(TNFR1) triggers bind(TNFR1,TRADD)

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General syntax to represent networks

• e causes f if f1 fk
• g1 gk causes g
• h1 hm n_triggers e
• k1 kl triggers e
• r1 rl inhibits e
• e is an event (also referred to as an action) and
  the rest are fluents (properties of the cell)
• For metabolic interactions
• e converts g1 gk to f1 fk if h1 hm

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Semantics queries and entailment

• Observation part of queries
• f at t
• a occurs_at t
• Given the Network N and observation O
• Predict if a temporal expression holds.
• Explain a set of observations.
• Plan to achieve a goal.

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Importance of a formal semantics

• Besides defining prediction, explanation and
  planning, it is also useful in identifying
• Under what restrictions the answer given by a
  given (graph based) algorithm will be correct.
  (soundness!)
• Under what restrictions a given (graph based)
  algorithm will find a correct answer if one
  exists. (completeness!)

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Utility of declarative programming languages
(such as AnsProlog)

• Allows for quick implementation of the semantics
• The specification or the definition of what is an
  explanation, or what is a plan becomes a program
  that finds explanations and plans respectively.

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Prediction

• Given some initial conditions and observations,
  to predict how the world would evolve or predict
  the outcome of (hypothetical) interventions.

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Back to the example

• Binding of TNF-a with TNFR1 leads to TRADD
  binding with one or more of TRAF2, FADD, RIP.
• TRADD binding with TRAF2 leads to over-expression
  of FLIP provided NIK is phosphorylated on the
  way.
• TRADD binding with RIP inhibits phosphorylation
  of NIK.
• TRADD binding with FADD in the absence of FLIP
  leads to cell death.

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Prediction 1.

• Binding of TNF-a with TNFR1 leads to TRADD
  binding with one or more of TRAF2, FADD, RIP.
• TRADD binding with TRAF2 leads to over-expression
  of FLIP provided NIK is phosphorylated on the
  way.
• TRADD binding with RIP inhibits phosphorylation
  of NIK.
• TRADD binding with FADD in the absence of FLIP
  leads to cell death.

• Initial Condition
• bind(TNF-a,TNF-R1) occurs at t0
• Query
• predict eventually apoptosis
• Answer
• Unknown!
• Incomplete knowledge about the TRADDs bindings.
• Depends on if bind(TRADD, RIP) happened or not!

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Prediction 2

• Binding of TNF-a with TNFR1 leads to TRADD
  binding with one or more of TRAF2, FADD, RIP.
• TRADD binding with TRAF2 leads to over-expression
  of FLIP provided NIK is phosphorylated on the
  way.
• TRADD binding with RIP inhibits phosphorylation
  of NIK.
• TRADD binding with FADD in the absence of FLIP
  leads to cell death.

• Initial Condition
• bind(TNF-a,TNF-R1) occurs at t0
• Observation
• TRADDs binding with TRAF2, FADD, RIP
• Query
• predict eventually apoptosis
• Answer Yes!

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Explanation

• Given initial condition and observations, to
  explain why final outcome does not match
  expectation.

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Explanation 1

• Binding of TNF-a with TNFR1 leads to TRADD
  binding with one or more of TRAF2, FADD, RIP.
• TRADD binding with TRAF2 leads to over-expression
  of FLIP provided NIK is phosphorylated on the
  way.
• TRADD binding with RIP inhibits phosphorylation
  of NIK.
• TRADD binding with FADD in the absence of FLIP
  leads to cell death.

• Initial condition
• bound(TNF-a,TNFR1) at t0
• Observation
• bound(TRADD, TRAF2) at t1
• Query Explain apoptosis
• One explanation
• Binding of TRADD with RIP
• Binding of TRADD with FADD

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Planning

• Given initial conditions, to plan interventions
  to achieve a goal.
• Application in drug and therapy design.

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Planning requirements

• In addition to the knowledge about the pathway we
  need additional information about possible
  interventions such as
• What proteins can be introduced
• What mutations can be forced.

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Planning example

• Defining possible interventions
• intervention intro(DN-TRAF2)
• intro(DN-TRAF2) causes present(DN-TRAF2)
• present(DN-TRAF2) inhibits bind(TRAF2,TRADD)
• present(DN-TRAF2) inhibits interact(TRAF2,NIK)
• Initial condition
• bound(NF?B,I?B) at 0
• bind(TNF-a,TNF-R1) at 0
• Goal to keep NF?B remain inactive.
• Query
• plan always bound(NF?B,I?B) from 0

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Conclusion of part 1

• From paper in ISMB 2004
• Our goal in this paper was to make progress
  towards developing a system (and the necessary
  representation language and reasoning algorithms)
  that can be used to represent signal networks and
  pathways associated with cells and reason with
  them.
• A start was made.
• Defined a simple language (syntax and semantics)
• Defined prediction, planning and explanation
• A prototype implementation using AnsProlog
• Illustration of its applicability with respect to
  an NFkB pathway.

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Issues with incomplete knowledge

• Often one may not be able to do much predication,
  explanation or planning.
• What then?
• Can reasoning help in obtaining new knowledge?
• Yes, through hypothesis generation!
• In fact, hypothesis generation needs reasoning!

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Part II Hypothesis Generation
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Hypothesis generation

• Our observations can not be explained by our
  existing knowledge OR the explanations given by
  our existing knowledge are invalidated by
  experiments?
• Conclusion Our knowledge needs to be augmented
  or revised?
• How?
• Can we use a reasoning system to predict some
  hypothesis that one can verify through
  experimentation?
• Automate the reasoning in the minds of a
  biologist, especially helpful when the background
  knowledge is humongous.

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Knowledge base
UV leads_to cancer
High UV
Hypothesis space
(K,I) O
p53
Cancer
No cancer
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Issues in this tiny example

• Hypothesis formation
• Theory UV leads to cancer.
• Observation wild-type p53 resists the UV
  effect.
• Hypothesis p53 is a tumor-suppressor.
• Elaboration tolerance
• How do we update/revise UV leads to cancer?
• Default NM reasoning
• Normally UV leads to cancer.
• UV does not lead to cancer if p53 is present.

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Related Works some prior mention of hypothesis
formation

• HYPGENE (Karp, 1991)
• TRANSGENE (Darden, 1997)
• GenePath (Zupan et al., 2003)
• Robot Scientist (King et al., 2004)
• Database (Doherty et al., 2004)
• BIOCHAM (Calzone et al., 2005)
• PathLogic (Karp et al. 2002)
• Cytoscape (Shannon et al., 2003)
• Integrative Scheme (Su et al., 2003)
• Pathway Analysis (Ingenuity?)
• do not use the latest advances in knowledge
  representation and reasoning. (eg. lack of ways
  to express defaults, non-monotonicity,
  elaboration tolerance, problem solving rules,
  etc.)

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Hypothesis formation

• Knowledge base K
• Set of initial conditions I
• Set of (experimental) observations O
• (K,I) does not entail O
• To expand (K,I) to (K, I) (K, I) entails
  O
• How to expand (hypothesis space)
• Explanation expand only I
• Diagnosis normality assumptions about I,
  minimally abandon the normality assumptions
• Hypothesis formation expand K

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Construction of hypothesis space

• Present manual construction, using research
  literature
• Future integration of multiple data sources
• Protein interactions
• Pathway databases
• Biological ontologies
• ..
• Provide cues, hunches such as
• A may interact with B action interact(A,B)
• A-B interaction may have effect C
• interact(A,B) causes C

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Generation of hypotheses

• Enumeration of hypotheses
• Search computing with Smodels (an implementation
  of AnsProlog)
• Heuristics
• A trigger statement is selected only if it is the
  only cause of some action occurrence that is
  needed to explain the novel observations.
• An inhibition statement is selected only if it is
  the only blocker of some triggered action at some
  time.
• Maximizing preferences of selected statements

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Generation (cont) heuristics

• Knowledge base K
• a causes g
• b causes g
• Initial condition I intially f
• Observation O eventually g
• (K,I) does not entail O
• Hypothesis space to expand K with rules among
• f triggers a
• f triggers b
• Hypotheses f triggers a , or f
  triggers b

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Case study p53 network
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Tumor suppression by p53

• p53 has 3 main functional domains
• N terminal transactivator domain
• Central DNA-binding domain
• C terminal domain that recognizes DNA damage
• Appropriate binding of N terminal activates
  pathways that lead to protection of cell from
  cancer.
• Inappropriate binding (say to Mdm2) inhibits p53
  induced tumor suppression.

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p53 knowledge base

• Stress
• high(UV ) triggers upregulate(mRNA(p53))
• Upregulation of p53
• upregulate(mRNA(p53)) causes high(mRNA(p53))
• high(mRNA(p53)) triggers translate(p53)
• translate(p53) causes high(p53)

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p53 knowledge base (cont.)

• Tumor suppression by p53
• high(p53) inhibits growth(tumor)

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p53 knowledge base (cont)

• Interaction between Mdm2 and p53
• high(p53), high(mdm2) triggers bind(p53,mdm2)
• bind(p53,mdm2) causes bound(dom(p53,N))
• bind(p53,mdm2) causes high(p53 mdm2),
• bind(p53,mdm2) causes high(p53),high(mdm2)

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Hypothesis formation

• Experimental observation
• I initially high(UV), high(mdm2), high(ARF)
  
• O eventually tumorous
• (K,I) does not entail O
• Need to hypothesize the role of ARF.

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Constructing hypothesis space

• Levels of ARF and p53 correlate
• high(ARF) triggers upregulate(mRNA(p53))
• high(p53) triggers upregulate(mRNA(ARF))

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Constructing (cont)

• Interactions of ARF with the known proteins
• bind(p53,ARF) causes bound(dom(p53,N))

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Constructing (cont)

• Influence of X (ARF) on other interactions
• high(ARF) triggers upreg(mRNA(p53))
• high(ARF) triggers translate(p53)
• high(ARF) triggers bind(p53,mdm2)

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Twelve Generated Hypothesis such as

• high(UV) triggers upregulate(mRNA(ARF))
• high(ARF), high(mdm2) triggers bind(ARF,mdm2)

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Conclusion of part 2

• Goal Automation of hypothesis formation (with
  respect to interactions and pathways)
• Approach Viewed known qualitative aspects of
  cell activities as a knowledge base
• Used knowledge representation language that
• Can express defaults
• Allows reasoning with incomplete knowledge
• Can express reasoning as well as problem solving
  rules
• Developed a system BioSigNet-RRH
• Formalizing and reasoning about hypotheses
• Illustration Hypothesizing the role of ARF
  protein in the p53 network.

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Future Work on Reasoning about Biochemical
Networks (Part I and II)

• Further development of the language
• Validation with respect to larger networks
• Kohns map
• Networks in Reactome and other repositories
• Going from prototype to deployable systems
• Scaling up challenges
• Recent advances in automatic planning
• Integration with Biopax

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Part III CBioC

• http//cbioc.org

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Do we have enough knowledge in the various
databases

• Some have been curated into databases.
• But there is much more in the literature.
• So what do we do?

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Current status of curation from text

• About 15 million abstracts in Pubmed
• 3 million published by US and EU researchers
  during 1994-2004 (800 articles per day)
• 300 K articles published so far reporting
  protein-protein interactions in human, yeast and
  mouse.
• BIND (in 7 yrs) -- 23K DIP 3K MINT 2.4K.

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Premise High cost of human curation

• Overwhelming cost of large curation efforts may
  be unsustainable for long periods
• BIND Nov 2005 bad news.
• Operated for 7 years
• Listed over 100 curators programmers
• CND 29 million received in 2003, plus other
  funding
• Curation efforts of AFCS has recently stopped.
• Lack of funding for some genome annotation
  projects.

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Premise summary

• Human curation of text is expensive.
• Human curation of text is not scalable.
• Human curation of text is not sustainable.

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Why not resort to computers? do automatic
extraction

• Lessons from DARPA funded MUCs (message
  understanding conferences) in 90s for a decade
  and at the cost of tens of millions of dollars.
• Getting to 60 recall and precision is quick
• Then every 5 improvement is about a years work.
• Even when we get to 90 for an individual entity
  extraction
• for recognizing 4 related entities (.9)4 .64
• Lessons from Biomedical text extraction
• No proper evaluation.
• Recognized that recall and precision is not very
  good even in the best systems.

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What do we do?

• How do we curate not only the existing articles,
  but also the future articles?
• Too important to give up!
• Need to think of a new way to do it.
• Faster computers, better sequencing technology
  and better algorithms came to the rescue of the
  Human Genome project.
• Hmm. What resources are we overlooking?

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Key Idea

• If lots of articles are being written then lot of
  people are writing them and lot of people are
  reading them.
• If only we could make these people (the authors
  and the readers) contribute to the curation
  effort
• Especially the readers the ones who need the
  curated data!

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Mass collaboration has worked in

• Wikipedia
• Project Gutenberg
• Netflix rating
• Amazon rating
• Etc.

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Mass collaborative curation initial hurdles

• An average reader
• (S)he is not normally interested in filling a
  blank curation form.
• We can not make an average reader go though
  curation training.
• So it has to be very different from just making
  the existing curation tools available to the mass
  and expect them to contribute.

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Mass collaborative curation key initial ideas

• Make it very easy
• user need not remember where (which database,
  which web page) to put the curated knowledge.
• Curation opportunity should present itself
  seamlessly.
• Curation should not be a burden to an average
  user
• Make the curated knowledge thin.
• There should be immediate rewards
• Do not start with a blank slate.

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Realization of the key ideas a biologist with a
gene name

• Goes to Pubmed, types the gene name, clicks on
  one of the abstracts
• Curation panel presents itself automatically
• Our approach calls for researchers to contribute
  to the curation of facts as they read and
  research over the web
• But not with a blank slate
• No one wants to be the first one!
• Automatic extraction jump-starts the process, and
  then researchers improve upon the extracted data,
  ironing out inconsistencies by subsequent edits
  on a massive scale.
• Thin Schemas
• Average users turned off by traditional wide
  schemas
• Wide schemas need to be broken down.

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Summary

• Information/curation window pops up
  automatically.
• Automatic extraction is used as a boot strap so
  that no user is working on a blank slate.
• Users vote on correctness, make corrections, add
  fact.
• Suppose 60 precision and recall of automatic
  extraction system
• A person will have an easier time discarding 40
  of wrongly extracted text than identifying 60 of
  correct entries and entering them!

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Very useful byproducts

• Avoids some problems with existing human curation
  approach
• Curators bias
• Curators miss things
• Curators have disagreements
• Slow access to newest findings
• Researchers at large have little or no control
  over what gets curated and when
• A large curated corpus of text gets created
• Very useful to evaluate and improve automated
  extraction systems.

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Current status of CBioC future plans

• Basic system, as described, is ready
• Being populated with
• Facts from existing databases (BIND etc.)
• Facts extracted using our extraction system
• Querying mechanism
• Answer display
• Future work
• Voter confidence issues

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Conclusion

• Collecting what is known
• Reasoning with what is known
• Hypothesizing what is unknown (based
  on observations)

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Open Invitation

• We are building and eager to help other groups
  build knowledge bases in particular domains to
• Predict impact of interventions
• Plan (therapy design) to make a pathway behave in
  a desired way
• Explain observation
• Hypothesize new knowledge
• Further improvements to and adaptation of CBioC

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Acknowledgements

• BioSignet
• Nam Tran, Ph.D thesis on this, Postdoc _at_ Yale
• Karen Chancellor, Ph.D student
• Michael Berens and his group (Ana Joy, Nhan Tran)
• Lokesh Joshi and his group (Vinay Nagraj)
• CBioc Graciela Gonzalez, Lian Yu, Luis Tari,
  Tony Gitter, Amanda Ziegler, Ryan Wendt,
  Prabhdeep Singh.
• Other projects
• BioQA
• Biogenenet

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Thank you!