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Details

  • Name

    Elsa Ferreira Gomes
  • Cluster

    Computer Science
  • Role

    Affiliated Researcher
  • Since

    01st November 2016
Publications

2022

MigraR: An open-source, R-based application for analysis and quantification of cell migration parameters

Authors
Shaji, N; Nunes, F; Rocha, MI; Gomes, EF; Castro, H;

Publication
Computer Methods and Programs in Biomedicine

Abstract
Background and objective: Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics. Methods: This report describes MigraR, an intuitive graphical user interface executed from the RStudioTM (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness. Results: MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software. Conclusions: Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced. © 2021 Elsevier B.V.

2022

Approaches to manage and understand student engagement in programming

Authors
Tavares, PC; Gomes, EF; Henriques, PR; Vieira, DM;

Publication
Open Education Studies

Abstract
Abstract Computer Programming Learners usually fail to get approved in introductory courses because solving problems using computers is a complex task. The most important reason for that failure is concerned with motivation; motivation strongly impacts on the learning process. In this paper we discuss how techniques like program animation, and automatic evaluation can be combined to help the teacher in Computer Programming courses. In the article, PEP system will be introduced to explain how it supports teachers in classroom and how it engages students on study sessions outside the classroom. To support that work, students’ motivation was studied; to complement that study, a survey involving students attending the first year of Algorithms and Programming course of an Engineering degree was done. It is also presented a tool to analyse surveys, using association rules.

2021

Automatic Identification of Bird Species from Audio

Authors
Carvalho, S; Gomes, EF;

Publication
Intelligent Information and Database Systems - 13th Asian Conference, ACIIDS 2021, Phuket, Thailand, April 7-10, 2021, Proceedings

Abstract

2021

The Usage of Data Augmentation Strategies on the Detection of Murmur Waves in a PCG Signal

Authors
Torres, J; Oliveira, J; Gomes, EF;

Publication
Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies

Abstract

2019

Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

Authors
Nogueira, DM; Ferreira, CA; Gomes, EF; Jorge, AM;

Publication
Journal of Medical Systems

Abstract

Supervised
thesis

2021

Deep learning para classificação automática de sons usando o Audioset

Author
MIGUEL ÂNGELO MOREIRA ROCHA

Institution
IPP-ISEP

2021

Deteção de patologia em sons cardíacos usando deep learning

Author
JOSÉ PEDRO INEZ DE MEIRA TORRES

Institution
IPP-ISEP

2020

Identificação automática de aves a partir de áudio

Author
SILVESTRE DANIEL DIAS CARVALHO

Institution
IPP-ISEP

2019

Análise e previsão de acidentes rodoviários usando data mining

Author
BRUNO MIGUEL FERREIRA TEIXEIRA

Institution
IPP-ISEP

2018

Deteção de arritmias cardíacas em eletrocardiogramas usando deep learning

Author
GABRIEL MOREIRA DA ROCHA

Institution
IPP-ISEP