Machine-Learning-Based Approach to Decode the Influence of Nanomaterial Properties on Their Interaction with Cells
Ajay Vikram Singh, Romi-Singh Maharjan, Anurag Kanase, Katherina Siewert, Daniel Rosenkranz, Rishabh Singh, Peter Laux, Andreas Luch
Abstract
phenotype adjustments. We used correlation function as a machine-learning algorithm to successfully predict cell and nuclei shapes and polarity functions as phenotypic markers for five different classes of nanomaterials studied herein this report. The CSI and NAF as nanodescriptors can be used as intuitive cell phenotypic parameters to define the safety of nanomaterials extensively used in consumer products and nanomedicine.
Topics & Concepts
NanomaterialsNanotoxicologyBiophysicsMaterials scienceContext (archaeology)NanotechnologyCellPhenotypeBiological systemNanoparticleZeta potentialChemistryBiochemistryBiologyGenePaleontologyNanoparticles: synthesis and applications