Renato Vimieiro

Renato Vimieiro
Federal University of Minas Gerais | UFMG · Departamento de Ciência da Computação

BSc, MSc, PhD

About

31
Publications
3,713
Reads
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215
Citations
Additional affiliations
January 2019 - present
Federal University of Minas Gerais
Position
  • Lecturer
August 2014 - January 2020
Federal University of Pernambuco
Position
  • Lecturer
August 2012 - April 2014
The University of Newcastle, Australia
Position
  • Research Academic

Publications

Publications (31)
Conference Paper
Full-text available
Automatic text summarization aims at condensing the contents of a text into a simple and descriptive summary. Summarization techniques drastically benefited from the recent advances in Deep Learning. Nevertheless, these techniques are still unable to properly deal with long texts. In this work, we investigate whether the combination of summaries ex...
Preprint
Full-text available
A variety of works in the literature strive to uncover the factors associated with survival behaviour. However, the computational tools to provide such information are global models designed to predict if or when a (survival) event will occur. When approaching the problem of explaining differences in survival behaviour, those approaches rely on (as...
Conference Paper
The Coronavirus disease 2019 (COVID-19) was first detected in China in December 2019. In a few months, the disease got pandemic proportions, overloading health systems all around the world. Risk factors related to the progression and outcome of the disease are still unclear. Moreover, clinical aspects of patients can differ between societies, and o...
Conference Paper
Full-text available
Twitter has been one of the main sources of information and discussion during the COVID-19 pandemics. This paper characterizes a set of more than 56 million tweets written in Portuguese and collected over a period of 70 days. Our analysis includes the volume of messages, text of tweets, location of tweets, the main elements of tweets (e.g. hashtags...
Chapter
Full-text available
The development of treatments based on the patient’s individual characteristics has been an emergent medical approach. The objective is to improve individual responses and overall survival. Thus, there is a need for computational tools able to identify and describe subgroups of patients for which the survival response significantly differs from the...
Article
This paper presents an evolutionary algorithm for Discriminative Pattern (DP) mining that focuses on high dimensional data sets. DPs aims to identify the sets of characteristics that better differentiate a target group from the others (e.g. successful vs. unsuccessful medical treatments). It becomes more natural to extract information from high dim...
Conference Paper
It is a great challenge to companies, governments and researchers to extract knowledge in high dimensional databases. Discriminative Patterns (DPs) is an area of data mining that aims to extract relevant and readable information in databases with target attribute. Among the algorithms developed for search DPs, it has highlighted the use of evolutio...
Article
Full-text available
Background: Multi-gene lists and single sample predictor models have been currently used to reduce the multidimensional complexity of breast cancers, and to identify intrinsic subtypes. The perceived inability of some models to deal with the challenges of processing high-dimensional data, however, limits the accurate characterisation of these subt...
Article
Full-text available
The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstr...
Article
Full-text available
We propose here a methodology to uncover modularities in the network of SNP-SNP interactions most associated with disease. We start by computing all possible Boolean binary SNP interactions across the whole genome. By constructing a weighted graph of the most relevant interactions and via a combinatorial optimization approach, we find the most high...
Article
Full-text available
In this paper we analyse the word frequency profiles of a set of works from the Shakespearean era to uncover patterns of relationship between them, highlighting the connections within authorial canons. We used a text corpus comprising 256 plays and poems from the 16th and 17th centuries, with 17 works of uncertain authorship. Our clustering approac...
Article
We focus, in this paper, on the computational challenges of identifying disjunctive Boolean patterns in high-dimensional data. We conduct our analysis focusing particularly in microarray gene expression data, since this is one of the most stereotypical examples of high-dimensional data. We devised a novel algorithm that takes advantage of the scarc...
Poster
Gene expression microarray data has expanded our understanding of breast cancer disease and also supported further classification in five distinct subtypes: luminal A, luminal B, HER2-enriched, normal-like, and basal-like [1,2]. The investigation of individual transcriptomic signatures remains a valuable tool to determine patient diagnosis and prog...
Article
We investigate in this paper the problem of mining disjunctive emerging patterns in high-dimensional biomedical datasets. Disjunctive emerging patterns are sets of features that are very frequent among samples of a target class, cases in a case–control study, for example, and are very rare among all other samples. We, for the very first time, demon...
Article
To study the influence of adjuvant androgen suppression and bisphosphonates on incident vertebral and non-spinal fracture rates and bone mineral density (BMD) in men with locally advanced prostate cancer. Between 2003 and 2007, 1071 men with locally advanced prostate cancer were randomly allocated, using a 2 × 2 trial design, to 6 months i.m. leupr...
Article
Disjunctive minimal generators were proposed by Zhao, Zaki, and Ramakrishnan (2006). They defined disjunctive closed itemsets and disjunctive minimal generators through the disjunctive support function. We prove that the disjunctive support function is compatible with the closure operator presented by Zhao et al. (2006). Such compatibility allows u...
Conference Paper
Due to their capability of dealing with nonlinear problems, artificial neural networks (ANN) are widely used with several purposes. Once trained, they are capable to solve unprecedented situations, keeping tolerable errors in their outputs. However, humans cannot assimilate the knowledge kept by those nets, since such knowledge is implicitly repres...
Conference Paper
Artificial Neural Networks (ANN) are widely used with several purposes Once trained, they are capable to solve unprecedented situations, keeping tolerable errors in their outputs However, humans cannot assimilate the knowledge kept by those nets, since such knowledge is implicitly represented by their connection weights Formal Concept Analysis (FCA...
Conference Paper
Due to their capability of dealing with nonlinear problems, artificial neural networks (ANN) are widely used with several purposes. Once trained, they are also capable of solving unprecedented situations, keeping tolerable errors in their outputs. However, ANN are considered essentially "black boxes". Therefore, humans can not assimilate the knowle...
Conference Paper
The artificial intelligence has been developed in order to represent human knowledge in computers systems. It has two main fields: the symbolic field that works with symbolic data; and the connectionist field whose main example is artificial neural network and whose main characteristic is the capacity of learning by data samples. To obtain a high a...
Conference Paper
Due to their capability of dealing with nonlinear problems, artificial neural networks (ANN) is widely used with several purposes. Once trained, they are capable to solve unprecedented situations, keeping tolerable errors in their outputs. However, humans cannot assimilate the knowledge kept by those nets, since such knowledge is implicitly represe...
Conference Paper
Alternative ways of energy producing are essential in a reality where natural resources have been scarce and solar collectors are one of these ways. However the mathematical modeling of solar collectors involves parameters that may lead to nonlinear equations. Due to their facility of solving nonlinear problems, ANN (i.e. Artificial Neural Networks...
Conference Paper
Due to the necessity of new ways of energy producing, solar collector systems have been widely used around the world. There are mathematical models that calculate the efficiency of those systems; however these models involve several parameters that may lead to nonlinear equations of the process. Artificial Neural Networks have been proposed in this...
Article
Since solar collectors have been presented as an alternative way of energy producing, many researches have been working with these systems. Due its facility in solving non-linear problems, Artificial Neural Networks(ANN) have been proposed, as a powerful tool, to represent solar energy systems, and specially solar collectors. Solar Energy systems a...
Article
Due to their capability of dealing with nonlinear problems, Artificial Neural Networks (ANN) are widely used with several purposes. Once trained, they are also capable of solving unprecedented situations, keeping tolerable errors in their outputs. However, humans can not assimilate the knowledge kept by those nets, since such knowledge is implicitl...
Article
Full-text available
This paper aims at presenting a performance evaluation of four re- presentative algorithms based on FCA for extracting association rules. The situations where each algorithm is less or more adequate will be discussed here. Resumo. Este artigo apresenta uma avaliação de desempenho de quatro algo- ritmos representativos baseados em AFC para extração...

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