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

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

Research output: Contribution to journalReview articleAcademicpeer-review

21 Citations (Scopus)
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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.
Original languageEnglish
Pages (from-to)743-755
Number of pages13
JournalTrends in Biotechnology
Issue number8
Publication statusPublished - Aug 2017


  • biomaterials
  • in silico screening
  • machine learning
  • modeling


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