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Trends Biotechnol. 2017 Aug;35(8):743-755. doi: 10.1016/j.tibtech.2017.05.007. Epub 2017 Jul 7.

How Not To Drown in Data: A Guide for Biomaterial Engineers.

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Laboratory for Cell Biology-Inspired Tissue Engineering, MERLN Institute for Technology-Inspired Regenerative Medicine, Maastricht University, Maastricht, The Netherlands.
Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA. Electronic address:


High-throughput assays that produce hundreds of measurements per sample are powerful tools for quantifying cell-material interactions. With advances in automation and miniaturization in material fabrication, hundreds of biomaterial samples can be rapidly produced, which can then be characterized using these assays. However, the resulting deluge of data can be overwhelming. To the rescue are computational methods that are well suited to these problems. Machine learning techniques provide a vast array of tools to make predictions about cell-material interactions and to find patterns in cellular responses. Computational simulations allow researchers to pose and test hypotheses and perform experiments in silico. This review describes approaches from these two domains that can be brought to bear on the problem of analyzing biomaterial screening data.


biomaterials; in silico screening; machine learning; modeling

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