A further development of the QNAR model to pbkp_redict the cellular uptake of nanoparticles by pancreatic cancer cells


Abstract:

Nanotechnology has led to the development of new nanomaterials with unique properties and a wide variety of applications. In the present study, we focused on the cellular uptake of a group of nanoparticles with a single metal core by pancreatic cancer cells, which has been studied by Yap et al. (Rsc Advances, 2012, 2 (2):8489–8496) using classification models. In this work, the development of a further Quantitative Nanostructure–Activity Relationship (QNAR) model was performed by linear multiple linear regression (MLR) and nonlinear artificial neural network (ANN) techniques to accurately pbkp_redict the cellular uptake values of these compounds by dividing them into three groups. Judging from the attained statistical results, our derived QNAR models have an acceptable overall accuracy and robustness, as well as good pbkp_redictivity on the external data sets. Moreover, the results of this study provide some insights on how engineered nanomaterial features influence cellular responses and thereby outline possible approaches for developing and applying pbkp_redictive computational models for biological responses caused by exposure to nanomaterials.

Año de publicación:

2018

Keywords:

  • Nanomaterials
  • Quantitative Nanostructure–Activity relationship (QNAR)
  • Radial basis function neural network (RBFNN)
  • Multiple linear regression method (MLR)

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso restringido

Áreas de conocimiento:

  • Cáncer
  • Cáncer

Áreas temáticas:

  • Enfermedades
  • Fisiología humana