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Despite its implementation in many industries, blockchain has never been harnessed to directly study biological mechanisms. Current uses of blockchain technology in biology and medicine has been limited to peripheral applications such as storing sequencing data or preventing tampering of clinical trial data. Although longstanding problems in computational biology mirror those addressed by blockchain, the technology has never been exploited to answer fundamental biological questions. Proposed here is a conceptual framework for employing blockchain technology to probe biological mechanisms. How principles of decentralization, synchronicity, immutability, and contracts can be utilized for cancer evolution and synthetic biology are explored.
blockchain, bepress|Engineering|Biomedical Engineering and Bioengineering|Systems and Integrative Engineering, engrXiv|Engineering|Biomedical Engineering and Bioengineering, bepress|Engineering, network biology, systems biology, bepress|Engineering|Biomedical Engineering and Bioengineering, Information technology, T58.5-58.64, Engineering, engrXiv|Engineering, engrXiv|Engineering|Biomedical Engineering and Bioengineering|Systems and Integrative Engineering, Systems and Integrative Engineering, cancer, genetics, Biomedical Engineering and Bioengineering
blockchain, bepress|Engineering|Biomedical Engineering and Bioengineering|Systems and Integrative Engineering, engrXiv|Engineering|Biomedical Engineering and Bioengineering, bepress|Engineering, network biology, systems biology, bepress|Engineering|Biomedical Engineering and Bioengineering, Information technology, T58.5-58.64, Engineering, engrXiv|Engineering, engrXiv|Engineering|Biomedical Engineering and Bioengineering|Systems and Integrative Engineering, Systems and Integrative Engineering, cancer, genetics, Biomedical Engineering and Bioengineering
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |