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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
023
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.

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.

2020

Evaluation of a temporal causal model for predicting the mood of clients in an online therapy

Authors
Becker, D; Bremer, V; Funk, B; Hoogendoorn, M; Rocha, A; Riper, H;

Publication
EVIDENCE-BASED MENTAL HEALTH

Abstract
Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.

2020

The ADC API: A Web API for the Programmatic Query of the AIRR Data Commons

Authors
Christley, S; Aguiar, A; Blanck, G; Breden, F; Chan Bukhari, SA; Busse, CE; Jaglale, J; Harikrishnan, SL; Laserson, U; Peters, B; Rocha, A; Schramm, CA; Taylor, S; Vander Heiden, JA; Zimonja, B; Watson, CT; Corrie, B; Cowell, LG;

Publication
Frontiers Big Data

Abstract

Supervised
thesis

2016

Platform for monitoring and treat depression

Author
José Pedro Alves Ornelas

Institution
UP-FCUP