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Presented By
Date
Timothy Hoctor, VP Professional Services
October 13, 2015
Introduction to Elsevier Professional Services &
Strategic Vision
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• Increase R&D productivity ‐ Support
research by linking R&D data across
development spectrum (discovery,
preclinical, clinical and patient
outcome)
• Increase return on information –
Enhanced search and visualization,
from “query‐to‐action”
• Define potential data standards
• Reduce cost of IT support by
implementing cloud technology and
extensive APIs that allows
customization with internal and
external sources
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Our Professional Services team leverages greater Elsevier capabilities to
provide customized and optimized data management & analysis solutions
• Customer CBI’s
(customer-provided)
• Lack of standards (for data and
metadata) and discipline (e.g. data
curation)
• Internal infrastructure is not designed
for the management, curation and
analysis of modern experimental data
• Difficulty to integrate CUSTOMER
data with public domain data in a
systematic way
• An acute shortage of people with
informatics and domain area (biology,
data mining)
• Integration is difficult even if we find
the data
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Strategic objective to become leading collaborator in R&D data
management
3
Data &
Process
Mapping
• Mapping of
External
Information
Chain against
business process
Gap Analysis
Data
Management
&
Stewardship
Setup
Data
Governance
and
Continuous
Data Quality
Improvement
Data
Normalization
and Long Term
Data Strategy
Where
we are
focusing
• Consulting
service on key
decision info gap
• Data linking
service and API
pilot to improve
decision making
• Data Management
Strategy
• Data warehouse
structure
• Taxonomy
integration and
implementation
• Data harmonization
• Collaborate with
business, technical
& project stewards
to design, develop,
standardize data
structure
• Instituting Master
Data management
• Be your external
data steward for
life science
community
• Data lifecycle
analytics and
management
service
End to end service with demonstrated R&D data harmonization, taxonomy
development, and life science information management expertise
Elsevier Life
Science
Professional
Services
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Professional Services takes siloed R&D data processes…
Data
Capture
Data
Storage
Data
Analysis
Data
Intelligence
…and transforms them into an integrated data management solution that
increases productivity and accelerates discovery.
Data
Capture
Data Organization
& Normalization
Integrated Data
Management
• Collective silo
• Aligned format
• Unlimited use potential
Data Analysis &
Intelligence
ELNs
Medicinal
Chemistry
Clinical
Trials
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Our Key Capabilities
• Access to life science data and
content with proven data curation,
taxonomy expertise, and semantic
backbone across R&D and post
market launch
• Demonstrated R&D data integration
expertise
• Existing life science portfolio of
solutions across development
spectrum
• Global footprint in life science sector
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Data Science and Translational R&D
• Integrated R&D data management with a
clear data stewardship strategy
• Broader utilization of available data
• Harmonization of internal, public and 3rd
party data to generate new scientific
insights and better business decisions
• Infrastructure to support handling of big data
and collaboration platform
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Case Study: Chemistry Data Management
Integration into Elsevier Chemistry platform: Roche,
Novartis
• Key Value Drivers:
• Increase Discoverability
• Efficiency: Decrease cost and maintenance
• High value in seeing failed reactions
• Don’t repeat them → proven savings!
• High value in cross-fertilization of Process Chem/Med
Chem
• Take advantage of designed Process Chem
experiments
• Improved patent filings time/content
• Make better use of resources → better chemistry
• Roche analyzed the time saved per scientist,
multiplied by wages and number of scientists
• ROI: Payback for the project cost was less than 1 year
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From Presentation at ACS 245th National Meeting by
Michael Kapler, Roche Pharma Research & Early Development
Integration with Customer Data Ecosystem: Merck
• Key Value Drivers:
• Efficiency: Decrease time and minimize
interface support
• Provide tailored workflows and broader use
cases
• Reduce discovery time and eliminate manual
curation
• Derive answers “from days to seconds”
• Eliminate lag in data currency
• Provide integrated content ‘dashboards’ to
suit multiple use cases alongside vertical
applications
• Support agile data framework
• Leverage experience to build on-demand
analysis tools (pre-defined query set against
normalized data)
From Presentation at BIOIT 2015 by
Huijun Wang, Merck R&D Chemistry
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MD Anderson
What did we do:
Provided a structured hosted biological data environment and
supporting services to normalize and analyze and compare
experimental data with data from across corpus of published
full text.
How we worked together:
• Beginning with extensive scoping and dedicated project
co-resourcing, through full documented use cases and
workflows and comparative analysis of semantic engine.
Environment(s) were tailored to specific needs and
populated with specifically defined with custom data
cartridges and taxonomies.
What was the result:
• MD Anderson system allowed for comparative analysis
against corpus of data from all major pathway analysis
tools deriving data from published literature, and then
further analysis against lab-generated internal data. Key
Benefit: Novel potential therapy and multiple unexplored
targets identified:
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Case Studies: Integrated Biology Data Management
DARPA Big Data Mechanism for Cancer Research
What did we do:
Provided a structured hosted biological data environment to
establish gold standard taxonomy and pattern recognition in
biological target validation for oncology
How we worked together:
• Worked in collaboration with academic partner (Carnegie
Mellon) to scope build and deploy enterprise platform for
comparative analytics and custom extensible oncology
data cartridge. Using iterative review for all project
participants to identify and incorporate best-in-class entity
and pattern recognition and gold standard data taxonomy
What was the result:
• DARPA system has been extended to more than 30 US
research groups for comparative analysis.
• Where explicit patterns have been identified outside the
system, these have been written into the system to
improve recall and causal reasoning.
• Taxonomy has been expanded to reflect new learnings
and improve relationship identification.
• DARPA has determined Elsevier’s system to be gold
standard for cancer pathway analytics.
From Presentation at BIOIT 2014 by
Phil Lorenzi, UT MD Anderson Cancer Center
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Executive Summary:
Elsevier performed research using Elsevier tools and data, public data source and
open source tools, to provide CUSTOMER with answers to specific research
questions, and presented back the research methodology. The following slides
specify questions posed by CUSTOMER to Elsevier for our collaboration
• Elsevier used our body of relationships mined from our large database of full text
using linguistic analysis to find subject-verb-object relationships in the text of the
articles
• The entire corpus of documents was mined to create a large database of
relationships that can be searched using simple or advanced search languages
• For all searches our extensive taxonomies and synonyms were used to normalize
terms and verbs for identifying relationships regardless of the exact words the
author used.
• Our team created custom taxonomies for protein purification methods as part of the
project
Project: Using Elsevier tools, data, and capabilities to address
research problems:
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• Method
• Searched for stated relationships between any biological element and Sjögren’s
syndrome.
• Used taxonomy of classifications to group the relationships into categories
• Findings
• Created ‘mind map’ of relationships of Sjögren’s syndrome to diseases, small-molecule
treatments, receptors, transcription factors, complexes, proteins
• Found leading researchers and institutions studying this disease
• Created collaboration maps showing collaborative studies, including pharma-academic,
an pharma-biotech collaborations.
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What is known about Sjögren’s syndrome? (e.g. Cell types,
pathways, highest confidence associated genes, etc…)
Question:
