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Details

  • Name

    Rui Camacho
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2011
003
Publications

2019

EvoPPI 1.0: a Web Platform for Within- and Between-Species Multiple Interactome Comparisons and Application to Nine PolyQ Proteins Determining Neurodegenerative Diseases

Authors
Vazquez, N; Rocha, S; Lopez Fernandez, H; Torres, A; Camacho, R; Fdez Riverola, F; Vieira, J; Vieira, CP; Reboiro Jato, M;

Publication
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES

Abstract
Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI (http://evoppi.i3s.up.pt) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.

2018

Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data

Authors
Rocha, A; Camacho, R; Ruwaard, J; Riper, H;

Publication
Internet Interventions

Abstract

2018

EvoPPI: A Web Application to Compare Protein-Protein Interactions (PPIs) from Different Databases and Species

Authors
Vázquez, N; Rocha, S; Fernández, HL; Torres, A; Camacho, R; Riverola, FF; Vieira, J; Vieira, CP; Jato, MR;

Publication
Practical Applications of Computational Biology and Bioinformatics, 12th International Conference, PACBB 2018, Toledo, Spain, 20-22 May, 2018.

Abstract
Biological processes are mediated by protein-protein interactions (PPI) that have been studied using different methodologies, and organized as centralized repositories - PPI databases. The data stored in the different PPI databases only overlaps partially. Moreover, some of the repositories are dedicated to a species or subset of species, not all have the same functionalities, or store data in the same format, making comparisons between different databases difficult to perform. Therefore, here we present EvoPPI (http://evoppi.i3s.up.pt), an open source web application tool that allows users to compare the protein interactions reported in two different interactomes. When interactomes belong to different species, a versatile BLAST search approach is used to identify orthologous/paralogous genes, which to our knowledge is a unique feature of EvoPPI. © Springer Nature Switzerland AG 2019.

2018

LearnSec: A Framework for Full Text Analysis

Authors
Goncalves, C; Iglesias, EL; Borrajo, L; Camacho, R; Seara Vieira, AS; Goncalves, CT;

Publication
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS (HAIS 2018)

Abstract
Large corpus of scientific research papers have been available for a long time. However, most of those corpus store only the title and the abstract of the paper. For some domains this information may not be enough to achieve high performance in text mining tasks. This problem has been recently reduced by the growing availability of full text scientific research papers. A full text version provides more detailed information but, on the other hand, a large amount of data needs to be processed. A priori, it is difficult to know if the extra work of the full text analysis has a significant impact in the performance of text mining tasks, or if the effect depends on the scientific domain or the specific corpus under analysis. The goal of this paper is to show a framework for full text analysis, called LearnSec, which incorporates domain specific knowledge and information about the content of the document sections to improve the classification process with propositional and relational learning. To demonstrate the usefulness of the tool, we process a scientific corpus based on OSHUMED, generating an attribute/value dataset in Weka format and a First Order Logic dataset in Inductive Logic Programming (ILP) format. Results show a successful assessment of the framework.

2018

Autoencoders as Weight Initialization of Deep Classification Networks Applied to Papillary Thyroid Carcinoma

Authors
Ferreira, MF; Camacho, R; Teixeira, LF;

Publication
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)

Abstract
Cancer is one of the most serious health problems of our time. One approach for automatically classifying tumor samples is to analyze derived molecular information. Previous work by Teixeira et al. compared different methods of Data Oversampling and Feature Reduction, as well as Deep (Stacked) Denoising Autoencoders followed by a shallow layer for classification. In this work, we compare the performance of 6 different types of Autoencoder (AE), combined with two different approaches when training the classification model: (a) fixing the weights, after pretraining an AE, and (b) allowing fine-tuning of the entire network. We also apply two different strategies for embedding the AE into the classification network: (1) by only importing the encoding layers, and (2) by importing the complete AE. Our best result was the combination of unsupervised feature learning through a single-layer Denoising AE, followed by its complete import into the classification network, and subsequent fine-tuning through supervised training, achieving an F1 score of 99.61% +/- 0.54. We conclude that a reconstruction of the input space, combined with a deeper classification network outperforms previous work, without resorting to data augmentation techniques.

Supervised
thesis

2017

Automatic comparison of interactomes

Author
Igor Guterres de Carvalho

Institution
UP-FEUP

2017

Identificação de Bioprocessos em textos

Author
Vânia Alice Sousa Leite

Institution
UP-FEUP

2017

Redes de co-expressão entre genes codificantes de proteínas mitocondriais e todos os restantes genes nos vários tecidos humanos

Author
João Alexandre Ribeiro de Almeida

Institution
UP-FEUP

2017

Deep Learning for genomic data analysis

Author
Vítor Filipe Oliveira Teixeira

Institution
UP-FEUP

2017

Previsão de efeitos adversos de medicamentos

Author
Jéssica Daniela Rocha Namora

Institution
UP-FCUP