elss use cases and strategy

TITLE OF PRESENTATION |

Presented By

Date

Timothy Hoctor, VP Professional Services

October 13, 2015

Introduction to Elsevier Professional Services & Strategic Vision


• 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


Strategic objective to become leading collaborator in R&D data management

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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

TITLE OF PRESENTATION | 4

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


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


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


A bit more detail: Merck

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Merck: Creating a data dashboard

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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


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:


• 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:

elss use cases and strategy

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