Pedro G. Ferreira

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 15

Top Topics

Symbolic Regression

Gene Expression

Machine Learning

Top Venues

IEEE Trans. Evol. Comput.

Briefings Bioinform.

Publications

Deaglan J. Bartlett, Benjamin D. Wandelt, Matteo Zennaro, Pedro G. Ferreira, Harry Desmond
syren-halofit: A fast, interpretable, high-precision formula for the ΛCDM nonlinear matter power spectrum. CoRR (2024)
Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
Exhaustive Symbolic Regression. IEEE Trans. Evol. Comput. 28 (4) (2024)
Tomás Sousa, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
The Simplest Inflationary Potentials. CoRR (2023)
Cláudia Brito, Pedro G. Ferreira, Bernardo Portela, Rui Carlos Oliveira, João Tiago Paulo
Privacy-Preserving Machine Learning on Apache Spark. IEEE Access 11 (2023)
Deaglan J. Bartlett, Lukas Kammerer, Gabriel Kronberger, Harry Desmond, Pedro G. Ferreira, Benjamin D. Wandelt, Bogdan Burlacu, David Alonso, Matteo Zennaro
A precise symbolic emulator of the linear matter power spectrum. CoRR (2023)
Athos Moraes, Marta Moreno, Rogério Ribeiro, Pedro G. Ferreira
Predicting Age from Human Lung Tissue Through Multi-modal Data Integration. DS (2023)
Harry Desmond, Deaglan J. Bartlett, Pedro G. Ferreira
On the functional form of the radial acceleration relation. CoRR (2023)
Delora Baptista, Pedro G. Ferreira, Miguel Rocha
A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer. PLoS Comput. Biol. 19 (3) (2023)
Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
Priors for symbolic regression. CoRR (2023)
Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
Priors for symbolic regression. GECCO Companion (2023)
Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
Exhaustive Symbolic Regression. CoRR (2022)
Marta Moreno, Ricardo Vilaça, Pedro G. Ferreira
Scalable transcriptomics analysis with Dask: applications in data science and machine learning. BMC Bioinform. 23 (1) (2022)
Delora Baptista, Pedro G. Ferreira, Miguel Rocha
Deep learning for drug response prediction in cancer. Briefings Bioinform. 22 (1) (2021)
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 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
The impact of rare variation on gene expression across tissues. Nat. 550 (7675) (2017)
Vitor Teixeira, Rui Camacho, Pedro G. Ferreira
Learning influential genes on cancer gene expression data with stacked denoising autoencoders. BIBM (2017)