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

How Not To Drown in Data: A Guide for Biomaterial Engineers 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Trends in Biotechnology Elsevier

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

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0167-7799
D.O.I.
10.1016/j.tibtech.2017.05.007
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Trends in BiotechnologyElsevier

Published: Aug 1, 2017

References

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