Translational bioinformatics embraces big data

Stanford University School of Medicine, 1265 Welch Road, Room X-229, Stanford, CA 94305, USA. E-mail: .
Yearbook of medical informatics 08/2012; 7(1):130-4.
Source: PubMed


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.

85 Reads
  • Source
    • "Since translational informatics is ready to revolutionize human health and healthcare using large-scale measurements on individuals [10], massive individual omics data will emerge as expected. Biospecimens are the physical sources of individual omics data. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Colorectal cancer is a leading cause of cancer mortality in both developed and developing countries. Transforming basic research results into clinical practice is one of the key tasks of translational research, which will greatly improve the diagnosis and treatments of colorectal cancer. In this paper, a translational research platform for colorectal cancer, named crcTRP, is introduced. crcTRP serves the colorectal cancer translational research by providing various types of biomedical information related with colorectal cancer to the community. The information, including clinical data, epidemiology data, individual omics data, and public omics data, was collected through a multisource biomedical information collection solution and then integrated in a clinic-omics database, which was constructed with EAV-ER model for flexibility and efficiency. A preliminary exploration of conducting translational research on crcTRP was implemented and worked out a set of clinic-genomic relations, linking clinical data with genomic data. These relations have also been applied to crcTRP to make it more conductive for cancer translational research.
    Full-text · Article · Jan 2013 · Computational and Mathematical Methods in Medicine
  • [Show abstract] [Hide abstract]
    ABSTRACT: Cancer epidemiology is at the cusp of a paradigm shift-propelled by an urgent need to accelerate the pace of translating scientific discoveries into health care and population health benefits. As part of a strategic planning process for cancer epidemiologic research, the Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI) is leading a "longitudinal" meeting with members of the research community to engage in an on-going dialogue to help shape and invigorate the field. Here, we review a translational framework influenced by "drivers" that we believe have begun guiding cancer epidemiology toward translation in the past few years and are most likely to drive the field further in the next decade. The drivers include: (i) collaboration and team science, (ii) technology, (iii) multilevel analyses and interventions, and (iv) knowledge integration from basic, clinical, and population sciences. Using the global prevention of cervical cancer as an example of a public health endeavor to anchor the conversation, we discuss how these drivers can guide epidemiology from discovery to population health impact, along the translational research continuum. Cancer Epidemiol Biomarkers Prev; 1-8. ©2013 AACR.
    No preview · Article · Jan 2013 · Cancer Epidemiology Biomarkers & Prevention
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of "big data." The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records.Genet Med advance online publication 5 September 2013Genetics in Medicine (2013); doi:10.1038/gim.2013.121.
    Full-text · Article · Sep 2013 · Genetics in medicine: official journal of the American College of Medical Genetics
Show more