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Bio-IT World Best Practices Awards
Advanced Deadline February 6, 2016
Celebrating Excellence in Innovation
The 2016 Bio-IT World Best Practices competition has released its call for entries. Bio-IT World has held the Best Practices awards since 2003, highlighting outstanding examples of technology innovation in the life sciences, from basic R&D to translational medicine. We particularly encourage vendors to nominate entries from valued academic and/or industry partners.
Submitting your organization’s entry is straightforward:
- Send us a brief overview of your technology, including a statement of the issue or problem at hand, the innovative approach or technology applied, and the ROI (Return on Investment) in terms of scientific insights, cost savings, productivity, etc. (entry form and full details here)
- Entries will be accepted until February 6, 2016.
- All entries will be judged by an expert panel in February/March 2016.
- Winners will be announced in a plenary session at Bio-IT World Conference & Expo, Boston, April 5- April 7, 2016.
- All winners will be featured in follow-up profiles in Bio-IT World.
Please send entries or direct any questions to Allison Proffitt.
2015 Best Practices Winners
The grand prize trophies were presented to the following organizations in these categories:
Clinical & Health IT: AstraZeneca and Tessella
Real Time Analytics for Clinical Trials (REACT)
AstraZeneca’s REACT system, implemented with input from Tessella, tracks vital statistics, laboratory tests and adverse events on both population and subject-specific levels during the course of a clinical trial. By collecting this information together in a platform with flexible visualization and query features, as well as a complete profile for each patient, REACT allows AstraZeneca to respond rapidly to safety concerns and swiftly identify risk factors for adverse events, protecting trial participants while rescuing trials from costly late-stage failures.
Research & Drug Discovery: U-BIOPRED
tranSMART-Based U-BIOPRED Study
Through the U-BIOPRED Knowledge Portal, built into the tranSMART platform for clinical and -omics data management, a consortium of over 30 partners from industry and academia were able to securely share longitudinal clinical, proteomic, and transcriptomic data on hundreds of respiratory disease patients in a search for key biomarkers.
Informatics: The Pistoia Alliance
Hierarchical Editing Language for Macromolecules (HELM)
HELM is a system of standardized naming for complex biomolecules, which defines these molecules as rigorously as chemists have long defined simpler compounds. By describing biomolecules through a hierarchical sequence of monomers, the open source HELM technology allows researchers and organizations to rapidly enter, share, and modify molecules without ambiguity.
IT Infrastructure & HPC: Baylor College of Medicine with DNAnexus
CHARGE: Large-Scale Genomic Analysis in the Cloud
To fulfill its role in the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium, Baylor College of Medicine undertook the largest genome analytics project in the cloud to date, using the DNAnexus platform. Using DNAnexus allowed Baylor to annotate 4,000 whole genomes and 11,000 whole exomes in record time, without monopolizing the local compute cluster.
Knowledge Management: Genentech
Genentech Cell Line Resource
Genentech’s centralized cell line bank contains over 1,800 cell lines, each represented by an average of 90,000 vials. By creating the gCell web platform to track and characterize these cell lines, Genentech has been able to create a genetic definition of each line, flag contamination and mislabeling, and curate new knowledge about individual cell lines to guide and optimize future research.
Judges’ Prize: UK National Health Service
Riak Deployment for Spine2 Distributed Database
The Spine database connects patient and services information across 27,000 British organizations that provide NHS care. By implementing Basho’s open source Riak database for the Spine2 overhaul of this system, the NHS can now instantaneously update information on any of the 80 million patients it serves, such that the new information is visible to any care provider, while maintaining a leaner and more flexible IT infrastructure that can be managed entirely in-house.
Editors’ Prize: The Icahn School of Medicine at Mt. Sinai with Ayasdi
Rethinking Type 2 Diabetes Through Topological Analysis
In analyzing medical records and genetic data on over 11,000 patients with Type 2 Diabetes, Mt. Sinai used Ayasdi’s insight discovery platform, which draws correlations between diverse data points through topological analysis. By clustering cases together based on features shared by specific cohorts, Mt. Sinai arrived at a novel hypothesis that Type 2 Diabetes should be treated as three separate conditions with distinct contributing factors. This analysis suggests a precision medicine approach may be beneficial for treating Type 2 Diabetes.
TrialNetworks’ clinical trial optimization system is a cloud-based platform brings everyone together—sponsors, sites, CROs and vendors—to speed the development and delivery of new drugs and therapies to patients. The platform features a unified suite of collaborative trial management apps and intuitive site tools to optimize key aspects of a trial: startup; enrollment; trial conduct; and closeout.
AstraZeneca’s Canvas 3 application helps researchers search for and collate information from various databases to identify and make decisions on a compound including corporate compound collections, Scifinder, TRCortellis, e-Lab notebooks, Pubmed, ACD, eMolecules, ChEBI, and Chemspider. Given any compound or drug name, structure or corporate ID, Canvas rapidly searches in-house and external data to answer these questions and others: Is the compound’s structure known? Is there a sample in the corporate collection? Is it commercially available? Is the compound proprietary, published in the literature, queued for synthesis or novel? Is it a drug, a controlled substance or under a corporate restriction? What are its physical properties? How might it be named in a patent? What biological data is available?