Details
Name
Vítor Santos CostaCluster
Computer ScienceRole
Senior ResearcherSince
01st January 2009
Nationality
PortugalCentre
Advanced Computing SystemsContacts
+351220402963
vitor.s.costa@inesctec.pt
2021
Authors
Oliveira, M; Torgo, L; Costa, VS;
Publication
Mathematics
Abstract
2021
Authors
Silva, C; da Silva, MF; Rodrigues, A; Silva, J; Costa, VS; Jorge, A; Dutra, I;
Publication
Recent Challenges in Intelligent Information and Database Systems - Communications in Computer and Information Science
Abstract
2021
Authors
Oliveira, M; Moniz, N; Torgo, L; Costa, VS;
Publication
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
Abstract
Extreme and rare events, such as spikes in air pollution or abnormal weather conditions, can have serious repercussions. Many of these sorts of events develop through spatio-temporal processes. Timely and accurate predictions are a most valuable tool in addressing their impact. We propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, which introduce bias into formerly random processes. This bias is a combination of a spatial and a temporal weight, which can be either static or relevance-aware, and includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposals provide an advantage over random resampling strategies in imbalanced numerical spatio-temporal forecasting tasks.
2021
Authors
Rosario Ferreira, N; Guimaraes, V; Costa, VS; Moreira, IS;
Publication
BMC BIOINFORMATICS
Abstract
Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.
2021
Authors
Rosário-Ferreira, N; Marques-Pereira, C; Pires, M; Ramalhão, D; Pereira, N; Guimarães, V; Santos Costa, V; Moreira, IS;
Publication
BioChem
Abstract
Supervised Thesis
2021
Author
Filipe Emanuel dos Santos Marinho da Rocha
Institution
UP-FCUP
2021
Author
Diogo Roberto de Melo e Diogo Machado
Institution
UP-FCUP
2021
Author
João Luis Alves Barbosa
Institution
UP-FCUP
2021
Author
Mariana Rafaela Figueiredo Ferreira de Oliveira
Institution
UP-FCUP
2021
Author
Victor Augusto Lopes Guimarães
Institution
UP-FCUP
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