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About

About

He is a senior researcher at INESC TEC since 1998. He is coordinator of HumanISE - Human-centered computing and Information Science

Current research interests include platforms and methods for collaborative research, privacy-preserving distributed computation, the semantic sensor Web (IoT) and Big Data processing.

From October 1996 to December 1997, he was an associate member of CERN - European Laboratory for High Energy Physics, IT Division/Web Office.

His research is applied in two major areas: Personalized Health Research (PHR) and Earth and Ocean Observation Science (EOOS).

The PHR area currently subdivides in: a) personalized Internet-based treatments; and b) human data storage, privacy-preserving processing and controlled FAIR data sharing. In this area, he participates in several European projects, such as ICT4Depression (FP7), E-COMPARED (FP7), STOP Depression (EEA Grant), iCare4Depression (FCT), RECAP Preterm (H2020), EUCAN-Connect (H2020) and iReceptor Plus (H2020). In these projects, he often undertakes the role of responsible for the system's architecture, platform implementation, or technical coordinator.

In the EOOS area he participates in the implementation of the RAIA Observatory (Interreg projects RAIA, RAIA.co, RAIA TEC, MarRisk and RADAR ON RAIA), SeaBioData(EEA Grant), MELOA (H2020) and C4G which is the Portuguese node of EPOS (H2020 EPOS-SP).

Interest
Topics
Details

Details

  • Name

    Artur Rocha
  • Cluster

    Computer Science
  • Role

    Centre Coordinator
  • Since

    02nd February 1998
026
Publications

2022

Development of a data classification system for preterm birth cohort studies: the RECAP Preterm project

Authors
Bamber, D; Collins, HE; Powell, C; Goncalves, GC; Johnson, S; Manktelow, B; Ornelas, JP; Lopes, JC; Rocha, A; Draper, ES;

Publication
BMC MEDICAL RESEARCH METHODOLOGY

Abstract
Background The small sample sizes available within many very preterm (VPT) longitudinal birth cohort studies mean that it is often necessary to combine and harmonise data from individual studies to increase statistical power, especially for studying rare outcomes. Curating and mapping data is a vital first step in the process of data harmonisation. To facilitate data mapping and harmonisation across VPT birth cohort studies, we developed a custom classification system as part of the Research on European Children and Adults born Preterm (RECAP Preterm) project in order to increase the scope and generalisability of research and the evaluation of outcomes across the lifespan for individuals born VPT. Methods The multidisciplinary consortium of expert clinicians and researchers who made up the RECAP Preterm project participated in a four-phase consultation process via email questionnaire to develop a topic-specific classification system. Descriptive analyses were calculated after each questionnaire round to provide pre- and post- ratings to assess levels of agreement with the classification system as it developed. Amendments and refinements were made to the classification system after each round. Results Expert input from 23 clinicians and researchers from the RECAP Preterm project aided development of the classification system's topic content, refining it from 10 modules, 48 themes and 197 domains to 14 modules, 93 themes and 345 domains. Supplementary classifications for target, source, mode and instrument were also developed to capture additional variable-level information. Over 22,000 individual data variables relating to VPT birth outcomes have been mapped to the classification system to date to facilitate data harmonisation. This will continue to increase as retrospective data items are mapped and harmonised variables are created. Conclusions This bespoke preterm birth classification system is a fundamental component of the RECAP Preterm project's web-based interactive platform. It is freely available for use worldwide by those interested in research into the long term impact of VPT birth. It can also be used to inform the development of future cohort studies.

2022

Moodbuster (E-MODEL): The feasibility of digital cognitive behavioural therapy (CBT) for depressed older adults: Study protocol of two pilot feasibility studies (Preprint)

Authors
Amarti, K; Schulte, MHJ; Kleiboer, AM; van Genugten, CR; Oudega, M; Sonnenberg, C; Gonçalves, GC; Rocha, A; Riper, H;

Publication

Abstract
BACKGROUND

Internet-based interventions can be effective in the treatment of depression. However, internet-based interventions for older adults with depression are scarce and little is known about their feasibility and effectiveness.

OBJECTIVE

To present the design of two studies aiming to assess the feasibility of internet-based cognitive behavioural treatment (CBT) for older adults with depression (E-MODEL). We will assess the feasibility of an online, guided version of E-MODEL among depressed older adults from the general population as well as the feasibility of a blended format (combining integrated face-to-face sessions and internet-based modules) in specialised mental health care outpatient clinic.

METHODS

A single-group pretest-posttest design will be applied for both settings. The primary outcome of the studies will be feasibility in terms of (a) acceptance and satisfaction (measured with the Client Satisfaction Questionnaire-8, (b) usability (measured with the System Usability Scale) and (c) engagement (measured with the Twente Engagement with Ehealth Technologies Scale). Secondary outcomes include: (a) severity of depressive symptoms (PHQ-8), (b) participant and therapist experience with the digital technology (with the use of qualitative interviews), (c) working alliance between patient and practitioner (from both perspectives; WAI-SF), (d) technical alliance between patient and the platform (WAI-TECH-SF) and (e) uptake in terms of attemped and completed modules. N=30 older adults with mild to moderate depressive symptoms (score between 5 and 11 as measured with the Geriatric Depression Scale 15) will be recruited from the general population. N=15 older adults with moderate to severe depressive symptoms (GDS-15 score between 8 and 15) will be recruited from a specialised mental health care outpatient clinic.

RESULTS

A mixed-method approach of quantitative and qualitative analyses will be adopted. Both the primary and secondary outcomes will be additionally explored with an individual semistructured interview and synthesized descriptively. Descriptive statistics (Mean and SDs) will be used to examine the primary and secondary outcome measures. Within-group depression severity will be analyzed using a two-tailed paired sample t-test to investigate differences between time points. The interviews will be recorded and analyzed using thematic analysis.

CONCLUSIONS

The results of this pilot study will show whether this platform is feasible among the older adult population in a blended and guided format in the two settings as well as a first exploration of the size of the effect of E-MODEL in terms of decrease of depressive symptoms.

2022

Feasibility of Digital Cognitive Behavioral Therapy for Depressed Older Adults With the Moodbuster Platform: Protocol for 2 Pilot Feasibility Studies

Authors
Amarti, K; Schulte, MHJ; Kleiboer, A; Van Genugten, CR; Oudega, M; Sonnenberg, C; Gonçalves, Gc; Rocha, A; Riper, H;

Publication
JMIR Research Protocols

Abstract

2021

Handling Privacy Preservation in a Software Ecosystem for the Querying and Processing of Deep Sequencing Data

Authors
Rocha, A; Costa, A; Oliveira, MA; Aguiar, A;

Publication
ERCIM NEWS

Abstract
iReceptor Plus will enable researchers around the world to share and analyse huge immunological distributed datasets, from multiple countries, containing sequencing data pertaining to both healthy and sick individuals. Most of the Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data is currently stored and curated by individual labs, using a variety of tools and technologies.

2021

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires

Authors
Pavlovic, M; Scheffer, L; Motwani, K; Kanduri, C; Kompova, R; Vazov, N; Waagan, K; Bernal, FLM; Costa, AA; Corrie, B; Akbar, R; Al Hajj, GS; Balaban, G; Brusko, TM; Chernigovskaya, M; Christley, S; Cowell, LG; Frank, R; Grytten, I; Gundersen, S; Haff, IH; Hovig, E; Hsieh, PH; Klambauer, G; Kuijjer, ML; Lund Andersen, C; Martini, A; Minotto, T; Pensar, J; Rand, K; Riccardi, E; Robert, PA; Rocha, A; Slabodkin, A; Snapkov, I; Sollid, LM; Titov, D; Weber, CR; Widrich, M; Yaari, G; Greiff, V; Sandve, GK;

Publication
NATURE MACHINE INTELLIGENCE

Abstract
Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

Supervised
thesis

2018

Desenvolvimento de Standard Operating Procedure para os testes em fábrica de armazéns automáticos

Author
Gil Miguel Mota Silva

Institution
UP-FEUP