Prachi Pradeep

发表

A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment

Daniel S. Sem, Craig A. Struble, Prachi Pradeep, 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Heuristics for scaling up distributed protein docking

Prachi Pradeep, 2011 .

Hybrid Computational Toxicology Models for Regulatory Risk Assessment

Prachi Pradeep, Prachi Pradeep, 2015 .

Navigating through the minefield of read-across tools: A review of in silico tools for grouping

Imran Shah, Prachi Pradeep, Grace Patlewicz, 2017 .

Evaluation of the Predictive Accuracy of QSAR Models and Alerts for Genotoxicity Using a Newly Compiled Experimental Dataset

Prachi Pradeep, Richard S. Judson, Grace Patlewicz, 2019 .

An ensemble model of QSAR tools for regulatory risk assessment

Richard J. Povinelli, Prachi Pradeep, Stephen J. Merrill, 2016, Journal of Cheminformatics.

MOESM1 of An ensemble model of QSAR tools for regulatory risk assessment

Richard J. Povinelli, Prachi Pradeep, Stephen J. Merrill, 2016 .

A Bayesian methodology for detecting targeted genes under two related experiments.

Prachi Pradeep, Hongmei Jiang, Naveen K Bansal, 2015, Statistics in medicine.

Estimating uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation

Prachi Pradeep, Richard S. Judson, John F. Wambaugh, 2019, Current Opinion in Toxicology.

Use of QSAR Modeling to Predict the Carcinogenicity of Color Additives

Prachi Pradeep, Stephen J. Merrill, Shannon M. White, 2013 .

Evaluation of Existing QSAR Models and Structural Alerts and Development of New Ensemble Models for Genotoxicity Using a Newly Compiled Experimental Dataset.

Prachi Pradeep, Grace Patlewicz, Maureen R. Gwinn, 2021, Computational toxicology.

A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols.

Kamel Mansouri, Prachi Pradeep, Grace Patlewicz, 2017, Computational toxicology.

Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels.

Prachi Pradeep, Sean Watford, Richard Judson, 2020, Computational toxicology.

Predicting estrogen receptor activation by a group of substituted phenols: An integrated approach to testing and assessment case study.

Richard S Judson, Prachi Pradeep, Grace Patlewicz, 2019, Regulatory toxicology and pharmacology : RTP.