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Literature DB >> 22890354

Translational bioinformatics embraces big data.

Abstract

We review the latest trends and major developments in translational bioinformatics in the year 2011-2012. Our emphasis is on highlighting the key events in the field and pointing at promising research areas for the future. The key take-home points are: • Translational informatics is ready to revolutionize human health and healthcare using large-scale measurements on individuals. • Data-centric approaches that compute on massive amounts of data (often called "Big Data") to discover patterns and to make clinically relevant predictions will gain adoption. • Research that bridges the latest multimodal measurement technologies with large amounts of electronic healthcare data is increasing; and is where new breakthroughs will occur.

Entities: Chemical Disease Species

Mesh：

Year: 2012 PMID： 22890354 PMCID： PMC4370941

Source DB: PubMed Journal: Yearb Med Inform ISSN： 0943-4747

48 in total

1. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm.

Authors: Paul Wicks; Timothy E Vaughan; Michael P Massagli; James Heywood
Journal: Nat Biotechnol Date: 2011-04-24 Impact factor: 54.908

2. Predicting adverse drug events from personal health messages.

Authors: Brant W Chee; Richard Berlin; Bruce Schatz
Journal: AMIA Annu Symp Proc Date: 2011-10-22

3. Predicting adverse drug events using pharmacological network models.

Authors: Aurel Cami; Alana Arnold; Shannon Manzi; Ben Reis
Journal: Sci Transl Med Date: 2011-12-21 Impact factor: 17.956

4. Using information mining of the medical literature to improve drug safety.

Authors: Kanaka D Shetty; Siddhartha R Dalal
Journal: J Am Med Inform Assoc Date: 2011-05-05 Impact factor: 4.497

5. Data-driven prediction of drug effects and interactions.

Authors: Nicholas P Tatonetti; Patrick P Ye; Roxana Daneshjou; Russ B Altman
Journal: Sci Transl Med Date: 2012-03-14 Impact factor: 17.956

6. Big Data: Large-Scale Historical Infrastructure from the Minnesota Population Center.

Authors: Matthew Sobek; Lara Cleveland; Sarah Flood; Patricia Kelly Hall; Miriam L King; Steven Ruggles; Matthew Schroeder
Journal: Hist Methods Date: 2011-01-01

7. Mining multi-item drug adverse effect associations in spontaneous reporting systems.

Authors: Rave Harpaz; Herbert S Chase; Carol Friedman
Journal: BMC Bioinformatics Date: 2010-10-28 Impact factor: 3.169

8. A timely arrival for genomic medicine.

Authors: Alan N Mayer; David P Dimmock; Marjorie J Arca; David P Bick; James W Verbsky; Elizabeth A Worthey; Howard J Jacob; David A Margolis
Journal: Genet Med Date: 2011-03 Impact factor: 8.822

9. Efficient replication of over 180 genetic associations with self-reported medical data.

Authors: Joyce Y Tung; Chuong B Do; David A Hinds; Amy K Kiefer; J Michael Macpherson; Arnab B Chowdry; Uta Francke; Brian T Naughton; Joanna L Mountain; Anne Wojcicki; Nicholas Eriksson
Journal: PLoS One Date: 2011-08-17 Impact factor: 3.240

10. The tell-tale heart: population-based surveillance reveals an association of rofecoxib and celecoxib with myocardial infarction.

Authors: John S Brownstein; Margarita Sordo; Isaac S Kohane; Kenneth D Mandl
Journal: PLoS One Date: 2007-09-05 Impact factor: 3.240

12 in total

Review 1. Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks. Contribution of the IMIA Primary Healthcare Working Group.

Authors: H Liyanage; S de Lusignan; S-T Liaw; C E Kuziemsky; F Mold; P Krause; D Fleming; S Jones
Journal: Yearb Med Inform Date: 2014-08-15

Translational bioinformatics embraces big data.

1. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm.

2. Predicting adverse drug events from personal health messages.

3. Predicting adverse drug events using pharmacological network models.

4. Using information mining of the medical literature to improve drug safety.

5. Data-driven prediction of drug effects and interactions.

6. Big Data: Large-Scale Historical Infrastructure from the Minnesota Population Center.

7. Mining multi-item drug adverse effect associations in spontaneous reporting systems.

8. A timely arrival for genomic medicine.

9. Efficient replication of over 180 genetic associations with self-reported medical data.

10. The tell-tale heart: population-based surveillance reveals an association of rofecoxib and celecoxib with myocardial infarction.

Review 1. Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks. Contribution of the IMIA Primary Healthcare Working Group.

Review 2. "Big data" and the electronic health record.

3. Big data and biomedical informatics: a challenging opportunity.

4. "Drivers" of translational cancer epidemiology in the 21st century: needs and opportunities.

5. Transcriptome marker diagnostics using big data.

Review 6. Some experiences and opportunities for big data in translational research.

7. crcTRP: a translational research platform for colorectal cancer.

Review 8. Toward a Literature-Driven Definition of Big Data in Healthcare.

9. Sharing big biomedical data.

Review 10. The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges.