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Publications

2020

Deep learning for drug response prediction in cancer

Authors
Baptista, D; Ferreira, PG; Rocha, M;

Publication
Briefings in Bioinformatics

Abstract
Abstract Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:mrocha@di.uminho.pt

2018

The effects of death and post-mortem cold ischemia on human tissue transcriptomes

Authors
Ferreira, PG; Munoz Aguirre, M; Reverter, F; Sa Godinho, CPS; Sousa, A; Amadoz, A; Sodaei, R; Hidalgo, MR; Pervouchine, D; Carbonell Caballero, J; Nurtdinov, R; Breschi, A; Amador, R; Oliveira, P; Cubuk, C; Curado, J; Aguet, F; Oliveira, C; Dopazo, J; Sammeth, M; Ardlie, KG; Guigo, R;

Publication
NATURE COMMUNICATIONS

Abstract
Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante-and postmortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.

2018

Cosmology and fundamental physics with the Euclid satellite

Authors
Amendola, L; Appleby, S; Avgoustidis, A; Bacon, D; Baker, T; Baldi, M; Bartolo, N; Blanchard, A; Bonvin, C; Borgani, S; Branchini, E; Burrage, C; Camera, S; Carbone, C; Casarini, L; Cropper, M; de Rham, C; Dietrich, JP; Di Porto, C; Durrer, R; Ealet, A; Ferreira, PG; Finelli, F; Garcia Bellido, J; Giannantonio, T; Guzzo, L; Heavens, A; Heisenberg, L; Heymans, C; Hoekstra, H; Hollenstein, L; Holmes, R; Hwang, ZQ; Jahnke, K; Kitching, TD; Koivisto, T; Kunz, M; La Vacca, G; Linder, E; March, M; Marra, V; Martins, C; Majerotto, E; Markovic, D; Marsh, D; Marulli, F; Massey, R; Mellier, Y; Montanari, F; Mota, DF; Nunes, NJ; Percival, W; Pettorino, V; Porciani, C; Quercellini, C; Read, J; Rinaldi, M; Sapone, D; Sawicki, I; Scaramella, R; Skordis, C; Simpson, F; Taylor, A; Thomas, S; Trotta, R; Verde, L; Vernizzi, F; Vollmer, A; Wang, Y; Weller, J; Zlosnik, T;

Publication
LIVING REVIEWS IN RELATIVITY

Abstract
Euclid is a European Space Agency medium-class mission selected for launch in 2020 within the cosmic vision 2015-2025 program. The main goal of Euclid is to understand the origin of the accelerated expansion of the universe. Euclid will explore the expansion history of the universe and the evolution of cosmic structures by measuring shapes and red-shifts of galaxies as well as the distribution of clusters of galaxies over a large fraction of the sky. Although the main driver for Euclid is the nature of dark energy, Euclid science covers a vast range of topics, from cosmology to galaxy evolution to planetary research. In this review we focus on cosmology and fundamental physics, with a strong emphasis on science beyond the current standard models. We discuss five broad topics: dark energy and modified gravity, dark matter, initial conditions, basic assumptions and questions of methodology in the data analysis. This review has been planned and carried out within Euclid's Theory Working Group and is meant to provide a guide to the scientific themes that will underlie the activity of the group during the preparation of the Euclid mission.

Supervised
thesis

2019

Transcriptomics-based prediction of human phenotypes using scalable and secure machine learning approaches

Author
Marta Carolina Cabral Moreno

Institution
UP-FCUP