How not to drown in data: a guide for biomaterial engineers

Aliaksei S. Vasilevich, Aurelie Carlier, Jan de Boer, Shantanu Singh (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschriftArtikel recenserenAcademicpeer review

20 Citaten (Scopus)
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Samenvatting

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.
Originele taal-2Engels
Pagina's (van-tot)743-755
Aantal pagina's13
TijdschriftTrends in Biotechnology
Volume35
Nummer van het tijdschrift8
DOI's
StatusGepubliceerd - aug 2017

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