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Integrated Bio-Search: challenges and trends for the integration, search and comprehensive processing of biological information

Overview of attention for article published in BMC Bioinformatics, January 2014
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96 Mendeley
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Title
Integrated Bio-Search: challenges and trends for the integration, search and comprehensive processing of biological information
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-s1-s2
Pubmed ID
Authors

Marco Masseroli, Barend Mons, Erik Bongcam-Rudloff, Stefano Ceri, Alexander Kel, François Rechenmann, Frederique Lisacek, Paolo Romano

Abstract

Many efforts exist to design and implement approaches and tools for data capture, integration and analysis in the life sciences. Challenges are not only the heterogeneity, size and distribution of information sources, but also the danger of producing too many solutions for the same problem. Methodological, technological, infrastructural and social aspects appear to be essential for the development of a new generation of best practices and tools. In this paper, we analyse and discuss these aspects from different perspectives, by extending some of the ideas that arose during the NETTAB 2012 Workshop, making reference especially to the European context. First, relevance of using data and software models for the management and analysis of biological data is stressed. Second, some of the most relevant community achievements of the recent years, which should be taken as a starting point for future efforts in this research domain, are presented. Third, some of the main outstanding issues, challenges and trends are analysed. The challenges related to the tendency to fund and create large scale international research infrastructures and public-private partnerships in order to address the complex challenges of data intensive science are especially discussed. The needs and opportunities of Genomic Computing (the integration, search and display of genomic information at a very specific level, e.g. at the level of a single DNA region) are then considered. In the current data and network-driven era, social aspects can become crucial bottlenecks. How these may best be tackled to unleash the technical abilities for effective data integration and validation efforts is then discussed. Especially the apparent lack of incentives for already overwhelmed researchers appears to be a limitation for sharing information and knowledge with other scientists. We point out as well how the bioinformatics market is growing at an unprecedented speed due to the impact that new powerful in silico analysis promises to have on better diagnosis, prognosis, drug discovery and treatment, towards personalized medicine. An open business model for bioinformatics, which appears to be able to reduce undue duplication of efforts and support the increased reuse of valuable data sets, tools and platforms, is finally discussed.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Spain 2 2%
Netherlands 2 2%
Portugal 1 1%
Germany 1 1%
France 1 1%
Unknown 85 89%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 20%
Student > Ph. D. Student 18 19%
Researcher 16 17%
Student > Bachelor 10 10%
Professor 7 7%
Other 14 15%
Unknown 12 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 24%
Computer Science 18 19%
Engineering 7 7%
Social Sciences 7 7%
Business, Management and Accounting 6 6%
Other 21 22%
Unknown 14 15%

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 26 September 2014.
All research outputs
#18,379,018
of 22,764,165 outputs
Outputs from BMC Bioinformatics
#6,307
of 7,273 outputs
Outputs of similar age
#228,996
of 305,046 outputs
Outputs of similar age from BMC Bioinformatics
#82
of 100 outputs
Altmetric has tracked 22,764,165 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 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 305,046 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.