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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, Vol. 799, 2014, p. 39-67.

Research output: SCORING: Contribution to journal › SCORING: Review article › Research

Harvard

Sulakhe, D, Balasubramanian, S, Xie, B, Berrocal, E, Feng, B, Taylor, A, Chitturi, B, Dave, U, Agam, G, Xu, J, Börnigen, D, Dubchak, I, Gilliam, TC & Maltsev, N 2014, 'High-throughput translational medicine: challenges and solutions', ADV EXP MED BIOL, vol. 799, pp. 39-67. https://doi.org/10.1007/978-1-4614-8778-4_3

APA

Sulakhe, D., Balasubramanian, S., Xie, B., Berrocal, E., Feng, B., Taylor, A., Chitturi, B., Dave, U., Agam, G., Xu, J., Börnigen, D., Dubchak, I., Gilliam, T. C., & Maltsev, N. (2014). High-throughput translational medicine: challenges and solutions. ADV EXP MED BIOL, 799, 39-67. https://doi.org/10.1007/978-1-4614-8778-4_3

Vancouver

Sulakhe D, Balasubramanian S, Xie B, Berrocal E, Feng B, Taylor A et al. High-throughput translational medicine: challenges and solutions. ADV EXP MED BIOL. 2014;799:39-67. https://doi.org/10.1007/978-1-4614-8778-4_3

Bibtex

@article{31efd3ab14c54795b5d6e66663dcdc26,
title = "High-throughput translational medicine: challenges and solutions",
abstract = "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.",
keywords = "Data Mining, Databases, Genetic, Genomics, Humans, Translational Medical Research, Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review",
author = "Dinanath Sulakhe and Sandhya Balasubramanian and Bingqing Xie and Eduardo Berrocal and Bo Feng and Andrew Taylor and Bhadrachalam Chitturi and Utpal Dave and Gady Agam and Jinbo Xu and Daniela B{\"o}rnigen and Inna Dubchak and Gilliam, {T Conrad} and Natalia Maltsev",
year = "2014",
doi = "10.1007/978-1-4614-8778-4_3",
language = "English",
volume = "799",
pages = "39--67",
journal = "ADV EXP MED BIOL",
issn = "0065-2598",
publisher = "Springer New York",

}

RIS

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 -