Big Data Analysis on Autopilot?. Williams, S. & Moore, J. 6(1):22+. ![Big Data Analysis on Autopilot? [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Biomedical sciences, especially fields such as genomics, are becoming Big Data fields, driven, to a large extent, simply by the ability to generate enormous data sets. For fields such as biology, where data has traditionally been small, the influx of Big Data, noise and all, has caused a need to rapidly shift research practices. Ways of dealing with these data have led to importing collaborators from statistics, computer science, and physics. However, these new "biologists" are simply not biologists and classically trained biologists are not "Big Data researchers". The confluence of these fields unfortunately has not led to the improvement of biological understanding via what should be synergy, but to a completely unfortunate shifted landscape of research. The new landscape involves Big Data use that reductionist biologists can comprehend quickly. Simply put, it has become the use of Big Data with a reductionist twist, or performing mostly reductionist analyses of Big Data. Of course, reductionism has been extremely fruitful in unraveling mechanisms for many key biological processes, but the translation of these processes into understanding of complex phenotypes, such as common human disease, has been less simple.
@article{williamsBigDataAnalysis2013,
title = {Big {{Data}} Analysis on Autopilot?},
author = {Williams, Scott and Moore, Jason},
date = {2013},
journaltitle = {BioData Mining},
volume = {6},
pages = {22+},
issn = {1756-0381},
doi = {10.1186/1756-0381-6-22},
url = {https://doi.org/10.1186/1756-0381-6-22},
abstract = {Biomedical sciences, especially fields such as genomics, are becoming Big Data fields, driven, to a large extent, simply by the ability to generate enormous data sets. For fields such as biology, where data has traditionally been small, the influx of Big Data, noise and all, has caused a need to rapidly shift research practices. Ways of dealing with these data have led to importing collaborators from statistics, computer science, and physics. However, these new "biologists" are simply not biologists and classically trained biologists are not "Big Data researchers". The confluence of these fields unfortunately has not led to the improvement of biological understanding via what should be synergy, but to a completely unfortunate shifted landscape of research. The new landscape involves Big Data use that reductionist biologists can comprehend quickly. Simply put, it has become the use of Big Data with a reductionist twist, or performing mostly reductionist analyses of Big Data. Of course, reductionism has been extremely fruitful in unraveling mechanisms for many key biological processes, but the translation of these processes into understanding of complex phenotypes, such as common human disease, has been less simple.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-12827339,automatic-knowledge-generation,big-data,computational-science-automation,data-transformation-modelling,epistemology,not-automatic-workflow,science-ethics},
number = {1}
}
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