TY - JOUR
T1 - Merging data curation and machine learning to improve nanomedicines
AU - Chen, Chen
AU - Yaari, Zvi
AU - Apfelbaum, Elana
AU - Grodzinski, Piotr
AU - Shamay, Yosi
AU - Heller, Daniel A.
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/4
Y1 - 2022/4
N2 - Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. “Big data” approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.
AB - Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. “Big data” approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.
KW - Artificial intelligence
KW - Cancer therapeutics
KW - Data curation
KW - Nanoparticles, data mining
KW - Nanotechnology
KW - Particle characterization
UR - http://www.scopus.com/inward/record.url?scp=85125526381&partnerID=8YFLogxK
U2 - 10.1016/j.addr.2022.114172
DO - 10.1016/j.addr.2022.114172
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C2 - 35189266
AN - SCOPUS:85125526381
SN - 0169-409X
VL - 183
JO - Advanced Drug Delivery Reviews
JF - Advanced Drug Delivery Reviews
M1 - 114172
ER -