NanoTox: Toxicity Modelling

Computational hazard assessment of MeOx nanoparticle toxicity using machine learning

Code files:

1. modelling.R: code for the core machine learning
2. vif.R: code for variance inflation factor analysis
3. normalise.R: code for normalisation
4. ApplicabilityDomain.R: code for applicability domain analysis
5. deployment.R: code for prediction of new instances

Data:

1. dataset.txt: dataset used in the study
2. model_***.RDS: RDS objects of the machine learning models created (namely Logistic, RandomForest, SVM, Neural Nets)
3. normalised_train_data.Rdata: R object of the normalised train data used in the study.

Citation:

Nilesh Anantha Subramanian, Ashok Palaniappan. NanoTox: Development of a Parsimonious In Silico Model for Toxicity Assessment of Metal-Oxide Nanoparticles Using Physicochemical Features. ACS Omega Article ASAP DOI: 10.1021/acsomega.1c01076

Correspondence: apalaniaATscbtDOTsastraDOTedu (A.P.)

Copyright (c) 2020 the Authors. MIT License