Title |
Towards human-computer synergetic analysis of large-scale biological data
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Published in |
BMC Bioinformatics, October 2013
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DOI | 10.1186/1471-2105-14-s14-s10 |
Pubmed ID | |
Authors |
Rahul Singh, Hui Yang, Ben Dalziel, Daniel Asarnow, William Murad, David Foote, Matthew Gormley, Jonathan Stillman, Susan Fisher |
Abstract |
Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 7 | 50% |
Comoros | 1 | 7% |
Uganda | 1 | 7% |
Unknown | 5 | 36% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 9 | 64% |
Scientists | 4 | 29% |
Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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China | 1 | 2% |
Slovenia | 1 | 2% |
Brazil | 1 | 2% |
Unknown | 49 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 10 | 19% |
Student > Ph. D. Student | 8 | 15% |
Student > Bachelor | 7 | 13% |
Researcher | 7 | 13% |
Student > Doctoral Student | 4 | 8% |
Other | 11 | 21% |
Unknown | 5 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 10 | 19% |
Agricultural and Biological Sciences | 9 | 17% |
Biochemistry, Genetics and Molecular Biology | 5 | 10% |
Engineering | 4 | 8% |
Business, Management and Accounting | 3 | 6% |
Other | 12 | 23% |
Unknown | 9 | 17% |