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Related Experiment Video

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Merging data curation and machine learning to improve nanomedicines.

Chen Chen1, Zvi Yaari2, Elana Apfelbaum3

  • 1Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Tri-institutional Ph.D. Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.

Advanced Drug Delivery Reviews
|February 21, 2022
PubMed
Summary

Data science and machine learning accelerate nanomedicine research by predicting material properties. Integrating data curation with data analytics in nanoinformatics can significantly advance nanomedicine development.

Keywords:
Artificial intelligenceCancer therapeuticsData curationNanoparticles, data miningNanotechnologyParticle characterization

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Area of Science:

  • Nanomedicine
  • Data Science
  • Computational Biology

Background:

  • Nanomedicine design traditionally relies on extensive trial-and-error experimentation.
  • Optimizing nanomedicine formulations and in vivo performance demands significant laboratory work.
  • Data science offers a promising avenue to accelerate nanomedicine research and development.

Purpose of the Study:

  • To review current advancements in data analytics for nanomedicine.
  • To highlight the role of machine learning in predicting nanomaterial properties and efficacy.
  • To explore the potential of integrating data curation with data analytics in nanoinformatics.

Main Methods:

  • Review of current literature on data science applications in nanomedicine.
  • Analysis of machine learning algorithms for predicting synthesis and biological behavior.
  • Examination of data curation strategies for standardizing heterogeneous datasets.

Main Results:

  • Machine learning algorithms are increasingly used to predict nanomedicine synthesis, pharmacologic parameters, and efficacy.
  • Big data approaches hold potential for significant advances, contingent on effective data curation.
  • Current efforts in data curation and data analytics within nanoinformatics are largely independent.

Conclusions:

  • Coordinated efforts between data curation and data analytics are crucial for advancing nanomedicine.
  • Leveraging nanoinformatics can streamline nanomedicine design and optimization.
  • Integrating these fields will enhance the predictive power and efficiency of nanomedicine research.