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Systems Analysis of Human Multigene Disorders

Overview of attention for book
Attention for Chapter 3: High-throughput translational medicine: challenges and solutions.
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Chapter title
High-throughput translational medicine: challenges and solutions.
Chapter number 3
Book title
Systems Analysis of Human Multigene Disorders
Published in
Advances in experimental medicine and biology, December 2013
DOI 10.1007/978-1-4614-8778-4_3
Pubmed ID
Book ISBNs
978-1-4614-8777-7, 978-1-4614-8778-4
Authors

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, 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, 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

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 2%
Unknown 41 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 21%
Student > Ph. D. Student 8 19%
Professor > Associate Professor 6 14%
Other 5 12%
Student > Bachelor 3 7%
Other 7 17%
Unknown 4 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 24%
Computer Science 8 19%
Medicine and Dentistry 8 19%
Biochemistry, Genetics and Molecular Biology 4 10%
Decision Sciences 1 2%
Other 2 5%
Unknown 9 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 April 2014.
All research outputs
#18,371,293
of 22,754,104 outputs
Outputs from Advances in experimental medicine and biology
#3,304
of 4,927 outputs
Outputs of similar age
#231,874
of 306,951 outputs
Outputs of similar age from Advances in experimental medicine and biology
#112
of 165 outputs
Altmetric has tracked 22,754,104 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,927 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 306,951 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 165 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.