ReConceptualizing LiteratureBased Discovery

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Re-Conceptualizing Literature-Based Discovery
Neil R. Smalheiser March 29, 2008
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What is LBD? A strategy for uncovering novel
hypotheses

• advocated by Don Swanson
• Magnesium-migraine, Fish oil-Raynauds
• The key idea is putting together
• explicit assertions from different papers to form
  new implicit assertions
• Regardless of how this is done, how the implicit
  assertions are assessed, whether the implicit
  assertions are correct!

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What is LBD? A routine way of life for scientists

• greatly under-recognized! Not just background
  reading, not just identifying anomalies or
  critical incidents that appear (explicitly) in
  a paper
• Since 1996 8 papers with Swanson, 40 without
  (i.e. non-one node search),
• 24 are biological (i.e. non-informatics
  modeling) 9/24 3/8 gt 1/3
• Proteins in unexpected locations (Molec. Biol.
  Cell, 1996)
• Expression of reelin in the blood (PNAS, 2000)
• Reelin and schizophrenia (PNAS 2000)
• Fluoxetine and neurogenesis (Eur. J. Pharmacol.
  2001)
• RNAi and memory (Trends in Neurosci. 2001)
• Bath toys (New Engl. J. Med. 2003)
• Dicer and calpain (J. Neurochem. 2005)
• Exosomal transfer of proteins RNAs at synapses
  (Biol Direct, 2007)
• microRNA machinery and regulation by
  phosphorylation (BBA, 2008)

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What is LBD? A body of research articles,
software and websites

• mostly by information scientists and computer
  scientists
• Mostly concerned with open discovery or one
  node searches, begin with a set of articles A
  that represents a problem
• Mostly use B-terms present in A to expand the
  search, find disparate lits Ci that share B-terms
  with A
• Try to find the Ci that is
• disparate yet most similar to A

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What is LBD? other researchers employ implicit
information too

• Bioinformatics
• gene-gene interactions
• protein-protein interactions
• web search
• author disambiguation
• text mining
• Yet these are not viewed as examples of LBD for
  some reason!

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Has the LBD field stagnated and not fulfilled
its promise?

• Kostoff critique(s)
• what is a discovery vs. an innovation
• argues against frequency based ranking,
• Uses very high recall, hundreds of discoveries
  claimed per question
• Swansons legacy Sw refs ended 2001!
• Bork review refs Sw ended 1996!
• Few gold standards are available (Mg, fish oil
  worn out)
• Combinatorial explosion A B C search method
• Impossible standards for what counts as a LBD
  prediction (never considered, never tested, must
  shatter a paradigm but must be proven
  experimentally??)
• Excluding active approaches other than one node
  search as being LBD

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Well, what DO we know about progress in LBD?

• The two-node search
• http//arrowsmith.psych.uic.edu
• Begin with two lits A and C that represent a
  known finding or a hypothesis (estrogen-AD)
• look for meaningful links
• (whether or not A and C are disparate)
• We use B-terms extracted from titles
• Could use abstracts, MeSH, triples

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Modeling the Two-Node Search-1

• Field testers, free-form use of the tool
• Chose 6 two-node searches as gold standards not
  too big or small, disparate, topically coherent,
  clean questions
• E.g. for A retinal detachment, C aortic
  aneurysm, a) find diseases in which both features
  appear not necessarily in same person or b)
  find surgical procedures that have been applied
  to both conditions.
• Manually marked relevant B-terms for a given
  query (sometimes several queries for the same two
  node search)
• Details in Bioinformatics (2007) paper

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Modeling the Two-Node Search-2

• Used 8 complementary features to score each
  B-term (e.g. recency, frequency, semantic
  categories)
• created a single combined and weighted score for
  each B-term
• Used logistic regression model to optimally give
  weights to each feature so as to separate marked
  relevant B-terms from all others (mixed set)

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Modeling the Two-Node Search-3
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Two End-Points of this Research

• For any two-node search, we can now rank the list
  of B-terms in order of estimated probability that
  they will be marked as relevant (meaningful) by
  SOME user for SOME query.
• For any pair of lits A and C,
• we can now estimate the OVERALL shared implicit
  information between A and C
• ( of B-terms that are predicted to be relevant)

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Relevance to the One Node Search

• We can re-conceptualize the one-node search as a
  series of two-node searches
• Choose A, then choose category C
• Divide category C into many small coherent Ci
  densely
• For each Ci, score multi-dimensional features
  Including, but not limited to, features that
  relate A to Ci (e.g. number of B-terms in common
  or predicted relevant B-terms)
• Rank the Ci to identify the most promising lits
  (which are presumed to point to novel hyps or
  implicit information helpful when applied to A)

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A is evaluated pairwise against C
C1 might involve B-terms C2 might
not! C3 C4 . e.g. A
Huntington Disease C lifestyle
factors autophagy, or therapeutic
agents
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Interestingness Measures

• Field of data mining.
• This allows us to encode real-life priorities and
  strategies of working scientists
• Existing one node search looks for novelty,
  relevance, non-trivial, likelihood of being true
  . get low hanging fruit
• What about actionability, feasibility of
  follow-up, surprisingness, cross-discipline,
  presence of high experimental support,
  generalizability to other problems, or high
  potential impact?
• A candidate Ci could be interesting because it is
  recently discovered and rapidly growing (e.g.
  microRNAs), well characterized, for a disease
  has an animal model, for a protein is connected
  to many other proteins, for a drug has FDA
  approval.
• not only re-conceptualizes one node search (e.g.,
  no combinatorial explosion) but it generalizes
  the ranking methods.

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Gold Standards for One-Node Searches

• Also, we can now envision preparing a series of
  gold standard searches, even automatically (cf.
  TREC 2006, 2007).
• Use implicit assertions to reconstruct explicit
  knowledge.
• Use review articles
• lists (e.g. in virus study, gold standard was a
  list of viruses that were thought to be at risk
  of being exploited for biological warfare).
• time slices
• Avoids the paradox that one node searches must
  predict things that have no experimental support!

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Conclusions

• LBD is (can be, will be) alive and well!
• Need to incorporate the types of real-life
  priorities and strategies of working scientists
• Re-conceptualize the one node search as a series
  of two-node searches
• Use interestingness measures to supplement
  B-term measures.

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Journal of Biomedical Discovery and Collaboration

• Unique multi-disciplinary audience
• People who engage in scientific discovery and
  collaboration
• People who make tools that enhance scientific
  discovery and collaboration
• People who study scientific discovery and
  collaboration
• Hosted by Biomed Central
• Fully peer-reviewed
• RAPID review (lt3 weeks is routine)
• Open-access, indexed in PubMed Central et al
• Readership goes up 10-100-fold
• Impact goes up too
• Article fee reduced or zeroed depending on
  institution

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Acknowledgements

• Don Swanson
• Vetle Torvik
• Wei Zhou (Clement Yu)
• Marc Weeber

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Ruminations

• Should LBD analyses be user-friendly? Popular??
• Dont they overlook true divergent discoveries?
• Should LBD be run automatically as a program in
  the background, with alerts of possible
  discoveries?
• Does LBD bypass, or reinforce, good old fashioned
  hypothesis driven science?