Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Luísa Castro

dblp:117/2652 · DBLP profile ↗
← Back
3ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0002-1312-0154ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3 · 1 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
genomics
0.212015
Three minimal sequences found in Ebola virus genomes and absent from human DNA · Bioinform. 2015
Bioinformatics and computational biology › genomics
pathogen genomics
0.212015
Three minimal sequences found in Ebola virus genomes and absent from human DNA · Bioinform. 2015
Bioinformatics and computational biology › sequence analysis › sequence comparison
alignment-free sequence analysis
0.112015
Three minimal sequences found in Ebola virus genomes and absent from human DNA · Bioinform. 2015

Methods — techniques the papers use, named apart from their topics

alignment-free method · 0.2
YearPublicationVenuePosition
2018 Towards FHR Biometric Identification: A Comparison between Compression and Entropy Based Approaches
abstract
In this study, fetal heart rate signal is used to exemplify the performance of compression and entropy based approaches in biometric identification. A total of 167 pairs of traces from real fetus are analyzed under the popular normalized compression distance, the recently proposed normalized relative compression measure and mutual information measure. The best performance was achieved with the normalized compression distance resulting in a misclassification rate of 12%. Fetal heart rate could be a relevant feature for biometric identification models, namely in multiple pregnancies.
Luísa Castro, Andreia Teixeira, Susana Brás, Cristina Costa-Santos
CBMS1
2015 Three minimal sequences found in Ebola virus genomes and absent from human DNA
abstract
Abstract Motivation: Ebola virus causes high mortality hemorrhagic fevers, with more than 25 000 cases and 10 000 deaths in the current outbreak. Only experimental therapies are available, thus, novel diagnosis tools and druggable targets are needed. Results: Analysis of Ebola virus genomes from the current outbreak reveals the presence of short DNA sequences that appear nowhere in the human genome. We identify the shortest such sequences with lengths between 12 and 14. Only three absent sequences of length 12 exist and they consistently appear at the same location on two of the Ebola virus proteins, in all Ebola virus genomes, but nowhere in the human genome. The alignment-free method used is able to identify pathogen-specific signatures for quick and precise action against infectious agents, of which the current Ebola virus outbreak provides a compelling example. Availability and Implementation: EAGLE is freely available for non-commercial purposes at http://bioinformatics.ua.pt/software/eagle. Contact: [email protected]; [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online.
Raquel M. Silva, Diogo Pratas, Luísa Castro, Armando J. Pinho, Paulo Jorge S. G. Ferreira
Bioinform.3
2005 Hierarchical clustering of multispectral images using combined spectral and spatial criteria
abstract
An agglomerative hierarchical clustering method, which uses both spectral and spatial information for the aggregation decision, is proposed here. The method is suitable for large multispectral images, provided that an unsupervised classification is previously applied. The method is tested on a synthetic image and on a satellite image of the coastal zone.
André R. S. Marçal, Luísa Castro
IEEE Geosci. Remote. Sens. Lett.2