Document Detail


High-throughput translational medicine: challenges and solutions.
MedLine Citation:
PMID:  24292961     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Recent technological advances in genomics now allow producing biological data at unprecedented tera- and petabyte scales. Yet, the extraction of useful knowledge from this voluminous data presents a significant challenge to a scientific community. Efficient mining of vast and complex data sets for the needs of biomedical research critically depends on seamless integration of clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships accumulated in a plethora of publicly available databases. Furthermore, such experimental data should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining. Translational projects require sophisticated approaches that coordinate and perform various analytical steps involved in the extraction of useful knowledge from accumulated clinical and experimental data in an orderly semiautomated manner. It presents a number of challenges such as (1) high-throughput data management involving data transfer, data storage, and access control; (2) scalable computational infrastructure; and (3) analysis of large-scale multidimensional data for the extraction of actionable knowledge.We present a scalable computational platform based on crosscutting requirements from multiple scientific groups for data integration, management, and analysis. The goal of this integrated platform is to address the challenges and to support the end-to-end analytical needs of various translational projects.
Authors:
Dinanath Sulakhe; Sandhya Balasubramanian; Bingqing Xie; Eduardo Berrocal; Bo Feng; Andrew Taylor; Bhadrachalam Chitturi; Utpal Dave; Gady Agam; Jinbo Xu; Daniela Börnigen; Inna Dubchak; T Conrad Gilliam; Natalia Maltsev
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Advances in experimental medicine and biology     Volume:  799     ISSN:  0065-2598     ISO Abbreviation:  Adv. Exp. Med. Biol.     Publication Date:  2014  
Date Detail:
Created Date:  2013-12-02     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0121103     Medline TA:  Adv Exp Med Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  39-67     Citation Subset:  IM    
Affiliation:
Computation Institute, University of Chicago/Argonne National Laboratory, 5735 S Ellis Ave, Chicago, IL, 60637, USA, sulakhe@mcs.anl.gov.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Characterizing Multi-omic Data in Systems Biology.
Next Document:  Computational approaches for human disease gene prediction and ranking.