Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About
Download Photo HD

About

Pedro G. Ferreira graduated in Systems and Informatics Engineering (2002) and completed a PhD in Artificial Intelligence from University of Minho (2007). He was a Postdoctoral Fellow at Center for Genomic Regulation, Barcelona (2008-2012) and at University of Geneva (2012-2014). He participated in several major international consortia including ICGC-CLL, ENCODE, GEUVADIS and GTEx. Currently, he is an Assistant Professor at the Department of Computer Science, Faculty of Sciences of University of Porto and a researcher at INESCTEC-LIADD and i3s/Ipatimup. His main research focus is in genomic data science. In particular, he is interested in unraveling the role of genomics on the human health and disease. He has been involved in several bioinformatics start-ups.

Interest
Topics
Details

Details

  • Name

    Pedro Gabriel Ferreira
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    20th September 2018
Publications

2021

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

2020

Gender Differential Transcriptome in Gastric and Thyroid Cancers

Authors
Sousa, A; Ferreira, M; Oliveira, C; Ferreira, PG;

Publication
FRONTIERS IN GENETICS

Abstract
Cancer has an important and considerable gender differential susceptibility confirmed by several epidemiological studies. Gastric (GC) and thyroid cancer (TC) are examples of malignancies with a higher incidence in males and females, respectively. Beyond environmental predisposing factors, it is expected that gender-specific gene deregulation contributes to this differential incidence. We performed a detailed characterization of the transcriptomic differences between genders in normal and tumor tissues from stomach and thyroid using Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data. We found hundreds of sex-biased genes (SBGs). Most of the SBGs shared by normal and tumor belong to sexual chromosomes, while the normal and tumor-specific tend to be found in the autosomes. Expression of several cancer-associated genes is also found to differ between sexes in both types of tissue. Thousands of differentially expressed genes (DEGs) between paired tumor-normal tissues were identified in GC and TC. For both cancers, in the most susceptible gender, the DEGs were mostly under-expressed in the tumor tissue, with an enrichment for tumor-suppressor genes (TSGs). Moreover, we found gene networks preferentially associated to males in GC and to females in TC and correlated with cancer histological subtypes. Our results shed light on the molecular differences and commonalities between genders and provide novel insights in the differential risk underlying these cancers.

2020

The GTEx Consortium atlas of genetic regulatory effects across human tissues

Authors
The GTEx Consortium; Dias Ferreira, PG;

Publication
Science

Abstract
The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

2020

Integrated Analysis of Structural Variation and RNA Expression of FGFR2 and Its Splicing Modulator ESRP1 Highlight the ESRP1(amp)-FGFR2(norm)-FGFR2-IIIc(high) Axis in Diffuse Gastric Cancer

Authors
Teles, SP; Oliveira, P; Ferreira, M; Carvalho, J; Ferreira, P; Oliveira, C;

Publication
CANCERS

Abstract
Gastric Cancer (GC) is one of the most common and deadliest types of cancer in the world. To improve GC prognosis, increasing efforts are being made to develop new targeted therapies. Although FGFR2 genetic amplification and protein overexpression in GC have been targeted in clinical trials, so far no improvement in patient overall survival has been found. To address this issue, we studied genetic and epigenetic events affecting FGFR2 and its splicing regulator ESRP1 in GC that could be used as new therapeutic targets or predictive biomarkers. We performed copy number variation (CNV), DNA methylation, and RNA expression analyses of FGFR2/ESRP1 across several cohorts. We discovered that both genes were frequently amplified and demethylated in GC, resulting in increased ESRP1 expression and of a specific FGFR2 isoform: FGFR2-IIIb. We also showed that ESRP1 amplification in GC correlated with a significant decreased expression of FGFR2-IIIc, an alternative FGFR2 splicing isoform. Furthermore, when we performed a survival analysis, we observed that patients harboring diffuse-type tumors with low FGFR2-IIIc expression revealed a better overall survival than patients with FGFR2-IIIc high-expressing diffuse tumors. Our results encourage further studies on the role of ESRP1 in GC and support FGFR2-IIIc as a relevant biomarker in GC.

2020

Predicting Gastric Cancer Molecular Subtypes from Gene Expression Data

Authors
Moreno, M; Sousa, A; Melé, M; Oliveira, R; G Ferreira, P;

Publication
Proceedings

Abstract

Supervised
thesis

2021

Analysis and comparison tool for multiple variant calling results from germline NGS data with clinical applications

Author
Francisco Manuel Oliveira de Almeida

Institution
UP-FCUP

2021

NRfinder: A pipeline for the characterization of the number and type of nuclear receptors in a genomic sequence

Author
Marcos António Pereira Domingues

Institution
UP-FCUP

2021

BioPredictor: a tool to predict the outcome of molecular alterations

Author
Marta Patrícia Ribeiro Ferreira

Institution
UP-FCUP

2021

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

Author
Marta Carolina Cabral Moreno

Institution
UP-FCUP

2020

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

Author
Marta Carolina Cabral Moreno

Institution
UP-FCUP