Pedro G. Ferreira

According to our database, Pedro G. Ferreira authored at least 13 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

syren-halofit: A fast, interpretable, high-precision formula for the ΛCDM nonlinear matter power spectrum.

Deaglan J. Bartlett, Benjamin D. Wandelt, Matteo Zennaro, Pedro G. Ferreira, Harry Desmond

CoRR, 2024

2023

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.

Delora Baptista, Pedro G. Ferreira, Miguel Rocha

PLoS Comput. Biol., March, 2023

A precise symbolic emulator of the linear matter power spectrum.

Deaglan J. Bartlett, Lukas Kammerer, Gabriel Kronberger, Harry Desmond, Pedro G. Ferreira, Benjamin D. Wandelt, Bogdan Burlacu, David Alonso, Matteo Zennaro

CoRR, 2023

The Simplest Inflationary Potentials.

Tomás Sousa, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira

CoRR, 2023

On the functional form of the radial acceleration relation.

Harry Desmond, Deaglan J. Bartlett, Pedro G. Ferreira

CoRR, 2023

Privacy-Preserving Machine Learning on Apache Spark.

Cláudia Brito, Pedro G. Ferreira, Bernardo Portela, Rui Carlos Oliveira, João Tiago Paulo

IEEE Access, 2023

Priors for symbolic regression.

Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Predicting Age from Human Lung Tissue Through Multi-modal Data Integration.

Athos Moraes, Marta Moreno, Rogério Ribeiro, Pedro G. Ferreira

Proceedings of the Discovery Science - 26th International Conference, 2023

2022

Exhaustive Symbolic Regression.

Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira

CoRR, 2022

Scalable transcriptomics analysis with Dask: applications in data science and machine learning.

Marta Moreno, Ricardo Vilaça, Pedro G. Ferreira

BMC Bioinform., 2022

2021

Deep learning for drug response prediction in cancer.

Delora Baptista, Pedro G. Ferreira, Miguel Rocha

Briefings Bioinform., 2021

2017

The impact of rare variation on gene expression across tissues.

Xin Li, Yungil Kim, Emily K. Tsang, Joe R. Davis, Farhan N. Damani, Colby Chiang, Gaelen T. Hess, Zachary Zappala, Benjamin J. Strober, Alexandra J. Scott, Amy Li, Andrea Ganna, Michael C. Bassik, Jason D. Merker, François Aguet, Kristin G. Ardlie, Beryl B. Cummings, Ellen T. Gelfand, Gad Getz, Kane Hadley, Robert E. Handsaker, Katherine H. Huang, Seva Kashin, Konrad J. Karczewski, Monkol Lek, Xiao Li, Daniel G. MacArthur, Jared L. Nedzel, Duyen T. Nguyen, Michael S. Noble, Ayellet V. Segrè, Casandra A. Trowbridge, Taru Tukiainen, Nathan S. Abell, Brunilda Balliu, Ruth Barshir, Omer Basha, Alexis J. Battle, Gireesh K. Bogu, Andrew Brown, Christopher D. Brown, Stephane E. Castel, Lin S. Chen, Donald F. Conrad, Nancy J. Cox, Olivier Delaneau, Emmanouil T. Dermitzakis, Barbara E. Engelhardt, Eleazar Eskin, Pedro G. Ferreira, Laure Frésard, Eric R. Gamazon, Diego Garrido-Martín, Ariel D. H. Gewirtz, Genna Gliner, Michael J. Gloudemans, Roderic Guigó, Ira M. Hall, Buhm Han, Yuan He, Farhad Hormozdiari, Cedric Howald, Hae Kyung Im, Brian Jo, Eun Yong Kang, Sarah Kim-Hellmuth, Tuuli Lappalainen, Gen Li, Boxiang Liu, Serghei Mangul, Mark I. McCarthy, Ian C. McDowell, Pejman Mohammadi, Jean Monlong, Stephen B. Montgomery

Nat., 2017

Learning influential genes on cancer gene expression data with stacked denoising autoencoders.

Vitor Teixeira, Rui Camacho, Pedro G. Ferreira

Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017