Literature-related discovery (LRD): Lessons
learned, and future research directions
Ronald N. Kostoff a, Joel A. Block b, Jeffrey L. Solka c, Michael B. Briggs d, Robert L. Rushenberg
e, Jesse A. Stump f, Dustin Johnson g, Terence J. Lyons h, Jeffrey R. Wyatt i
Technological Forecasting & Social Change 75 (2008) 276–299
a Office
of Naval Research, 875 N. Randolph St., Arlington, VA 22217, USA
b Section of Rheumatology, Rush Medical College, Rush University Medical Center, 1725 W. Harrison St., Suite 1017, Chicago, IL 60612, USA
c Naval Surface Weapons Center Dahlgren Division, Dahlgren, VA 22448-5100, USA, d Arlington, VA 22204, USA
e DDL-OMNI Engineering, LLC, 8260 Greensboro Drive, McLean, VA 22102, USA, f Catonsville, MD 21228, USA, g Arlington, VA 22201, USA
h Air Force Office of Scientific Research, Arlington, VA 22203, USA
i DDL-OMNI Engineering, LLC, 8260 Greensboro Drive, McLean, VA 22102, USA
Kathleen Padova, November 18, 2014
Publication
Technological Forecasting & Social Change v75 i2 (2008) Literature-Related Discovery - Edited by Ronald N. Kostoff
•
Literature-Related Discovery (LRD): Introduction and background, (165 – 185) Ronald N. Kostoff
•
Literature-related discovery (LRD): Methodology (186 – 202) Ronald N. Kostoff, Michael B.
Briggs, Jeffrey L. Solka, Robert L. Rushenberg
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Literature-related discovery (LRD): Potential treatments for Raynaud's Phenomenon (203 – 214),
Ronald N. Kostoff, Joel A. Block, Jesse A. Stump, Dustin Johnson
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Literature-related discovery (LRD): Potential treatments for cataracts (215 – 225) Ronald N.
Kostoff
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Literature-Related Discovery (LRD): Potential treatments for Parkinson's Disease (226 – 238),
Ronald N. Kostoff, Michael B. Briggs
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Literature-related discovery (LRD): Potential treatments for Multiple Sclerosis (239 – 255) Ronald
N. Kostoff, Michael B. Briggs, Terence J. Lyons
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Literature-related discovery (LRD): Water purification (256-275) Ronald N. Kostoff, Jeffrey L.
Solka, Robert L. Rushenberg, Jeffrey A. Wyatt
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Literature-related discovery (LRD): Lessons learned, and future research directions (276 - 299)
Ronald N. Kostoff, Joel A. Block, Jeffrey L. Solka, Michael B. Briggs, Robert L. Rushenberg, Jesse
A. Stump, Dustin Johnson, Terence J. Lyons, Jeffrey R. Wyatt
Post-publication
• Literature-related discovery, Annual Review of Information Science and
Technology Volume 43, Issue 1, pages 1–71, (2009)
• Literature-related discovery: Potential treatments and preventatives for
SARS, Technological Forecasting and Social Change, (2011)
• Literature-related discovery and innovation — update, Technological
Forecasting & Social Change 79 (2012)
• Literature-related discovery techniques applied to ocular disease: a
vitreous restoration example, Current opinion in ophthalmology, (2013)
• Literature-related discovery: Common factors for Parkinson's Disease and
Crohn's Disease, Scientometrics (2014)
• Literature-related discovery and innovation: Chronic kidney disease,
Technological Forecasting and Social Change in progress (Nov 2014)
Literature-Related Discovery
• Components
– Literature-based Discovery
– Literature-assisted Discovery
• Types
– Open Discovery Systems
– Closed Discovery Systems
LRD Literature Properties
• All the literatures should be complementary
• All the literatures should be disjoint
• All these literatures should be as
comprehensive as possible
• All these literatures with unique information
must be linked to form a whole that is greater
than the sum of its parts.
Summary of Generic Approach to ODS LRD
1.
2.
3.
4.
Retrieve core literature of target problem
Characterize core literature
Expand core literature
Generate potential discovery
Step 1: Retrieving Core Literature
• Very, very, very, important step
• Complete and comprehensive
• Query and Bibliometrics
Steps 2 & 3: Characterize core literature and expand
• Extract main topics from Step 1 using
clustering
• Use these new terms/phrases to expand the
literature set
Step 4: Discovery
• Identification of potential discovery
candidates
• Drawing the linkages between the potential
discovery candidates and the core literature
• Vetting
– Manually reviewing candidates, with experts
Approach for non-medical studies
• Doesn’t have the benefit of taxonomy
• Cluster Semantic Filtering
– Manually examination of clusters, select
semantically relevant
• Latent Semantic Indexing
– Tagged articles with core query terms
– Clustered into thematic group and used terms to
expand
– Ranked terms using similarity score
Study Applications
• Medical
– Reynaud’s Phenomenon
– Cataracts
– Parkinson’s Disease
– Multiple Sclerosis
• Non-medical
– Water Purification
Multiple sclerosis
•
•
•
•
•
Neurological disease
Auto-immune disease
Variety of symptoms
No cure
No known cause
Multiple Sclerosis Study Goals
1. Prevent the occurrence, or reduce the
progression rate, or stop the progression, or
maybe even reverse the progression, of MS
2. Demonstrate they could solve LRD problem
with no prior knowledge of results or prior
work
3. Generate large amounts of potential
discoveries in less time
Multiple Sclerosis Study Approach
• Retrieve Core MS Literature
– Query “Multiple Sclerosis”
• Directly related literature
– demyelination and remyelination; indirect
autoimmune contributors to inflammation/blood–
brain barrier; direct autoimmune contributors to
inflammation; myelin basic protein, and viruses,
diagnostic, treatment, symptoms, etc
• Auto-correlation maps and factor matrix
analysis
Multiple Sclerosis Factor Groups
•
•
•
•
•
•
Factor 1: Myelin sheath proteins and maintenance cells (myelin basic protein;
myelin oligodendrocyte glycoprotein*; proteolipid protein*; experimental
autoimmune encephalomyelitis; oligodendrocyte*; encephalitogenic; epitope*;
immunization; immunodominant)
Factor 2: Indirect contribution of lymphocytes to inflammation by secretion of
cytokines, chemokines, and lymphokines (cytokine*; tumor necrosis factor-alpha;
peripheral blood mononuclear cells; interferon-gamma; proinflammatory;
interleukin*)
Factor 3: Viral contributions to inflammation (murine encephalomyelitis virus; virus;
viral; demyelinating)
Factor 4: Autoimmunity (cerebrospinal fluid; oligoclonal IgG bands; isoelectric
focusing; glial fibrillary acidic protein; Intrathecal IgG; IgG index; immunofixation;
electrophoresis)
Factor 5: Demyelination and remyelination (oligodendrocyte*; demyelinate*; glial
fibrillary acidic protein; astrocyte*; axon*; myelin oligodendrocyte glycoprotein*;
remyelinat*)
Factor 6: Blood–brain barrier (vascular cell adhesion molecule-1; intercellular
adhesion; ICAM-1; endothelial; endothelial cell*; cell adhesion molecule;
endothelium).
Multiple Sclerosis Query Development
((demyelinate* OR remyelinat* OR “myelin sheath
pathology” OR (“myelin sheath” AND (damage OR
degenerate*)) OR “axonal loss” OR “axonal destruction” OR
(oligodendrocyte* AND (apoptosis OR death OR degenerate*
OR damage OR dystrophy)) OR (oligodendroglia* AND
(apoptosis OR destruct* OR loss))) OR (“blood–brain
barrier”AND (disruption OR “ell adhesion”OR “ctivated
lymphocyte*”OR “dhesion molecule*”OR (lymphocyte* AND
trafficking) OR breakdown OR transmigration OR
dissolution)))) NOT “multiple sclerosis”
Multiple Sclerosis Results
• Non-drug concepts within MS Core literature
–
–
–
–
–
–
Vitamin D
Green Tea
Qeuecetin
Caloric Restriction
Synthetic metal ion chelators
Specific herbal ingredients
• Non-drug potential discovery concepts in the directly
related literature
– Kalpaamruthaa – rheumatoid arthritis
– Salvia miltiorrhiza Bunge – atherosclerosis
– Inchinko TJ-135 - hepatitis
Multiple Sclerosis LRD Summary
• Synergy of lifestyle/dietary practices
• Non-discovery –
– Vitamin D, dietary chelators, caloric restriction, complementinhibitory herbs, Nigella sativa oil, green tea, and quercetin
• Potential Discovery –
– Shogaol, Ethanol, Iron, Petaslignolide A, Mangifera indica L,
Tiliroside, Gnaphaliin, Cissus quadrangularis extract,
Kalpaamruthaa, Salvia miltiorrhiza Bunge, Inchinko TJ-135,
Silymarin, Edaravone, Sopoongsan, and Artemesia iwayomogi
• Disconnection between mainstream and major medical
websites/journal review papers (core MS literature) and
related literature
Limitations of Approach to MS Study
• Not all possible semantic categories identified
• Relatively few terms selected from the queries
• Not all retrieved records were examined
• Skipped citations linking
Lessons Learned*
• Selecting the most important concepts from
extremely large volumes of potential discovery
retrieval
• Topical specialist(s) working closely with
information technologist(s)
• Functional query with clustering and semantic
filtering
• Bibliographic coupling
• Literature deficiencies major obstacle to progress
– Skewing of technical literature – treatment over cause
– Under-estimation of prior art
Literature-related discovery and innovation — update
• Technological Forecasting & Social Change 79
(2012) 789–800
• Ronald N. Kostoff –
Georgia Institute of Technology, School of Public
Policy
Literature-related discovery and innovation — update
• Literature-Related Discovery and Innovation
(LRDI)
• Compared results of previous LRDI study on MS
to a documented reversal of MS
– The Wahls Protocol: How I Beat Progressive
MS Using Paleo Principles and Functional
Medicine By Terry Wahls M.D., Eve Adamson
Literature-related discovery and innovation — update
• Past LRDI study identified potential causes and
potential non-drug treatments
• Dr. Wahls’ diet could have been obtained from
2007 literature set
• Literature never mentioned nutrition components
in conjunction with MS
• One of Dr Wahl’s treatments (NMES) was not
strong in literature
• LRDI itself is not provable, Dr. Wahls was personally
affected to follow treatment – ‘skin-in-the-game’
Discussion
• The LRD techniques presented by Kostoff were
still manually intensive, the techniques
presented by Spangler, et al. were more
systematic - how much human element can be
offloaded to computers?
• Kostoff’s LRDI techniques strongly depends on
controlled language and he discussed
techniques for studying topics without a
controlled vocabulary/taxonomy – are there
other approaches?