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Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights
Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different so...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182494/ https://www.ncbi.nlm.nih.gov/pubmed/25268270 http://dx.doi.org/10.1371/journal.pone.0108600 |
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author | Tsiliki, Georgia Karacapilidis, Nikos Christodoulou, Spyros Tzagarakis, Manolis |
author_facet | Tsiliki, Georgia Karacapilidis, Nikos Christodoulou, Spyros Tzagarakis, Manolis |
author_sort | Tsiliki, Georgia |
collection | PubMed |
description | Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence. |
format | Online Article Text |
id | pubmed-4182494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-41824942014-10-07 Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights Tsiliki, Georgia Karacapilidis, Nikos Christodoulou, Spyros Tzagarakis, Manolis PLoS One Research Article Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence. Public Library of Science 2014-09-30 /pmc/articles/PMC4182494/ /pubmed/25268270 http://dx.doi.org/10.1371/journal.pone.0108600 Text en © 2014 Tsiliki et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Tsiliki, Georgia Karacapilidis, Nikos Christodoulou, Spyros Tzagarakis, Manolis Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights |
title | Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights |
title_full | Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights |
title_fullStr | Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights |
title_full_unstemmed | Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights |
title_short | Collaborative Mining and Interpretation of Large-Scale Data for Biomedical Research Insights |
title_sort | collaborative mining and interpretation of large-scale data for biomedical research insights |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182494/ https://www.ncbi.nlm.nih.gov/pubmed/25268270 http://dx.doi.org/10.1371/journal.pone.0108600 |
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