P
Pedro Rodrigues
Researcher at University of Porto
Publications - 232
Citations - 8979
Pedro Rodrigues is an academic researcher from University of Porto. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 24, co-authored 176 publications receiving 7548 citations. Previous affiliations of Pedro Rodrigues include University of Minho & University of Lisbon.
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Journal ArticleDOI
Circulating vesicles hold etiology-related protein biomarkers of cholangiocarcinoma risk, early diagnosis and prognosis mirroring tumour cells
Ainhoa Lapitz,Mikel Azkargorta,Ekaterina Zhuravleva,Marit Mæhle Grimsrud,Colm O Rourke,Ander Arbelaiz,Adelaida La Casta,Mette Vesterhus,Piotr Milkiewicz,Malgorzata Milkiewicz,Raul Jimenez-Aguero,Tania Pastor,Rocio I.R. Macias,Ioana Riaño,Laura Izquierdo-Sanchez,Marcin Krawczyk,C. Ibarra,Javier Bustamante,Felix Elortza,Juan M. Falcón-Pérez,Maria J. Perugorria,Jesper B. Andersen,Luis Bujanda,Tom H. Karlsen,Trine Folseraas,Pedro Rodrigues,Jesus M. Banales +26 more
Journal ArticleDOI
Scavenger receptor marco is associated with an immunosuppressive microenvironment and tumor progression in intrahepatic cholangiocarcinoma
Alona Agirre-Lizaso,Maider Huici-Izagirre,Colm J O'Rourke,Ekaterina Zhuravleva,Ana Korosec,Mikel Azkargorta,Felix Elortza,Javier Vaquero,Sumera Ilyas,Gregory J. Gores,Jesper B. Andersen,Gernot Schabbauer,Luis Bujanda,Pedro Rodrigues,Omar Sharif,Jesus M. Banales,Maria J. Perugorria +16 more
Proceedings ArticleDOI
Bayesian Image Reconstruction for the Clear-PEM scanner
M.V. Martins,A. Trindade,Nuno Matela,Nuno Oliveira,H. Cordeiro,Pedro Rodrigues,Nuno Ferreira,J. Varela,Pedro Almeida +8 more
TL;DR: The results presented indicate that the use of the maximum a posteriori algorithm and the median root prior lead to a clear reduction in the noise of the images, without a significant loss of image spatial resolution or decrease in contrast.
Posted Content
Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements
TL;DR: In this article, a new forward model is proposed, specifically a new system of equations, requiring a few relatively sparse b-shells, and then applied modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert the proposed model.
Posted Content
HNPE: Leveraging Global Parameters for Neural Posterior Estimation.
TL;DR: In this article, a hierarchical neural posterior estimation (HNPE) method is proposed for cracking the indeterminacy of a stochastic model by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.