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Renata De Paris
Renata De Paris
Researcher, School of Technology, PUCRS
Verified email at acad.pucrs.br
Title
Cited by
Cited by
Year
Clustering molecular dynamics trajectories for optimizing docking experiments
RD Paris, CV Quevedo, DD Ruiz, ON Souza, RC Barros
Computational intelligence and neuroscience 2015, 32-32, 2015
542015
wFReDoW: a cloud-based web environment to handle molecular docking simulations of a fully flexible receptor model
R De Paris, FA Frantz, O Norberto de Souza, DDA Ruiz
BioMed research international 2013, 2013
372013
An effective approach for clustering InhA molecular dynamics trajectory using substrate-binding cavity features
R De Paris, CV Quevedo, DDA Ruiz, O Norberto de Souza
PloS one 10 (7), e0133172, 2015
292015
SmartIX: A database indexing agent based on reinforcement learning
G Paludo Licks, J Colleoni Couto, P de Fátima Miehe, R De Paris, ...
Applied Intelligence 50, 2575-2588, 2020
222020
A strategic solution to optimize molecular docking simulations using fully-flexible receptor models
CV Quevedo, R De Paris, DD Ruiz, ON de Souza
Expert Systems with Applications 41 (16), 7608-7620, 2014
192014
A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model
R De Paris, C Vahl Quevedo, DD Ruiz, F Gargano, ON de Souza
BMC bioinformatics 19, 1-16, 2018
122018
FReMI: a middleware to handle molecular docking simulations of fully-flexible receptor models in HPC environments
R De Paris
Pontifícia Universidade Católica do Rio Grande do Sul, 2012
82012
A cloud-based workflow approach for optimizing molecular docking simulations of fully-flexible receptor models and multiple ligands
R De Paris, DAD Ruiz, ON De Souza
2015 IEEE 7th International Conference on Cloud Computing Technology and …, 2015
42015
A conceptual many tasks computing architecture to execute molecular docking simulations of a fully-flexible receptor model
R De Paris, FA Frantz, O Norberto de Souza, DD Ruiz
Advances in Bioinformatics and Computational Biology: 6th Brazilian …, 2011
22011
eXplainable Artificial Intelligence on Medical Images: A survey
MVS da Silva, RR Arrais, JVS da Silva, FS Tânios, MA Chinelatto, ...
arXiv preprint arXiv:2305.07511, 2023
12023
An effective method to optimize docking-based virtual screening of fully-flexilbe receptor models
R De Paris, CV Quevedo, DA Ruiz, O Norberto de Souza
32nd Brazilian Symposium on Databases (SBBD). Minas Gerais: Brazilian …, 2017
12017
Clustering molecular dynamics trajectories with a univariate estimation of distribution algorithm
RC Barros, CV Quevedo, R De Paris, MP Basgalupp
2015 IEEE Congress on Evolutionary Computation (CEC), 2058-2065, 2015
12015
Efficient Brazilian Sign Language Recognition: A Study on Mobile Devices
VL Fabris, F de Castro Bastos, ACAM de Faria, JVNA da Silva, PA Luiz, ...
Iberoamerican Congress on Pattern Recognition, 406-419, 2023
2023
eXplainable Artificial Intelligence on Medical Images: A Survey
R Reis Arrais, JV Santos da Silva, F Souza Tânios, MA Chinelatto, ...
arXiv e-prints, arXiv: 2305.07511, 2023
2023
eXplainable Artificial Intelligence on Medical Images: A Survey (preprint)
MVS da Silva, RR Arrais, JVS da Silva, FS Tânios, MA Chinelatto, ...
2023
An effective method to optimize docking-based virtual screening in a clustered fully-flexible receptor model deployed on cloud platforms
R De Paris
Pontifícia Universidade Católica do Rio Grande do Sul, 2017
2017
Research Article Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments
R De Paris, CV Quevedo, DD Ruiz, ON de Souza, RC Barros
2015
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