High-throughput translational medicine: challenges and solutions
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High-throughput translational medicine: challenges and solutions. / Sulakhe, Dinanath; Balasubramanian, Sandhya; Xie, Bingqing; Berrocal, Eduardo; Feng, Bo; Taylor, Andrew; Chitturi, Bhadrachalam; Dave, Utpal; Agam, Gady; Xu, Jinbo; Börnigen, Daniela; Dubchak, Inna; Gilliam, T Conrad; Maltsev, Natalia.
in: ADV EXP MED BIOL, Jahrgang 799, 2014, S. 39-67.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Review › Forschung
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TY - JOUR
T1 - High-throughput translational medicine: challenges and solutions
AU - Sulakhe, Dinanath
AU - Balasubramanian, Sandhya
AU - Xie, Bingqing
AU - Berrocal, Eduardo
AU - Feng, Bo
AU - Taylor, Andrew
AU - Chitturi, Bhadrachalam
AU - Dave, Utpal
AU - Agam, Gady
AU - Xu, Jinbo
AU - Börnigen, Daniela
AU - Dubchak, Inna
AU - Gilliam, T Conrad
AU - Maltsev, Natalia
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Data Mining
KW - Databases, Genetic
KW - Genomics
KW - Humans
KW - Translational Medical Research
KW - Journal Article
KW - Research Support, N.I.H., Extramural
KW - Research Support, Non-U.S. Gov't
KW - Review
U2 - 10.1007/978-1-4614-8778-4_3
DO - 10.1007/978-1-4614-8778-4_3
M3 - SCORING: Review article
C2 - 24292961
VL - 799
SP - 39
EP - 67
JO - ADV EXP MED BIOL
JF - ADV EXP MED BIOL
SN - 0065-2598
ER -