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Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data
<span title="">2015</span>
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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/icbhosh775h7bgzgot6avm3cua" style="color: black;">BioMed Research International</a>
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Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2015/958302">doi:10.1155/2015/958302</a>
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<a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4411450/">pmcid:PMC4411450</a>
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... ure. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.
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