World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6232
World Ranking
10705
National Ranking
673

Overview

Paulo J. G. Lisboa is affiliated with Liverpool John Moores University in the United Kingdom. Their research spans multiple disciplines including Computer Science, Medicine, and Biochemistry, Genetics, and Molecular Biology. Within these broad fields, Lisboa's work frequently addresses subfields such as Artificial Intelligence, Molecular Biology, Cell Biology, Surgery, and Physical Therapy, Sports Therapy, and Rehabilitation.

Lisboa has a diverse range of research topics, reflecting interdisciplinary interests. Notable areas include Muscle Metabolism and Nutrition, Explainable Artificial Intelligence (XAI), Muscle Physiology and Disorders, Machine Learning and Data Classification, Balance, Gait, and Falls Prevention, Machine Learning in Healthcare, and Bayesian Modeling and Causal Inference.

Among their recent publications are:

  • The coming of age of interpretable and explainable machine learning models, 2023, Neurocomputing
  • A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities, 2020, Medical Engineering & Physics
  • Prediction of Balance Perturbations and Falls on Stairs in Older People Using a Biomechanical Profiling Approach: A 12-Month Longitudinal Study, 2020, The Journals of Gerontology Series A
  • Adaptation of rat fast-twitch muscle to endurance activity is underpinned by changes to protein degradation as well as protein synthesis, 2020, The FASEB Journal
  • Explainable inflation forecasts by machine learning models, 2022, Expert Systems with Applications

Lisboa has contributed to book publications as well, including authorship in "Deep Learning in Biology and Medicine" published by WORLD SCIENTIFIC (EUROPE) eBooks in 2021.

Their work is published in a variety of scientific venues, with multiple publications appearing in:

  • PLoS ONE
  • The FASEB Journal
  • ESANN 2021 proceedings
  • Scientific Reports
  • bioRxiv (Cold Spring Harbor Laboratory)

Lisboa frequently collaborates with several researchers, with repeated coauthorships including:

  • Sandra Ortega-Martorell
  • Iván Olier
  • Jatin G. Burniston
  • Ian H. Jarman
  • Connor A. Stead

Best Publications

  • The use of artificial neural networks in decision support in cancer: A systematic review

    Paulo J. Lisboa;Azzam F. G. Taktak

  • A review of evidence of health benefit from artificial neural networks in medical intervention

    P. J. G. Lisboa

  • Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers

    S.J. Perantonis;P.J.G. Lisboa

  • Making machine learning models interpretable

    Alfredo Vellido Alcacena;Jose D. Martin Guerrero;Paulo J.G. Lisboa

  • Segmentation of the on-line shopping market using neural networks

    A. Vellido;P.J.G. Lisboa;K. Meehan

  • A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

    P. J. G. Lisboa;H. Wong;P. Harris;R. Swindell

  • Quantitative characterization and prediction of on-line purchasing behavior: a latent variable approach

    Alfredo Vellido;Paulo J. G. Lisboa;Karon Meehan

  • The value of personalised recommender systems to e-business: a case study

    M. Benjamin Dias;Dominique Locher;Ming Li;Wael El-Deredy

  • Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach

    T.A. Etchells;P.J.G. Lisboa

  • Artificial Neural Networks in Biomedicine

    P. J. Lisboa;Piotr S. Szczepaniak;J. C. Mason;Emmanuel C. Ifeachor

  • Fuzzy systems in medicine

    Piotr S. Szczepaniak;Paulo J. G. Lisboa;Janusz Kacprzyk

  • Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection.

    Y. Huang;P. J. G. Lisboa;W. El-Deredy

  • Business applications of neural networks : the state-of-the-art of real-world applications

    Paulo J G Lisboa;Alfredo Vellido;Bill Edisbury

  • Gait quality assessment using self-organising artificial neural networks.

    Gabor Barton;Paulo Lisboa;Adrian Lees;Steve Attfield

  • Financial time series prediction using polynomial pipelined neural networks

    Abir Jaafar Hussain;Adam Knowles;Paulo J. G. Lisboa;Wael El-Deredy

  • Grocery shopping recommendations based on basket-sensitive random walk

    Ming Li;Benjamin M. Dias;Ian Jarman;Wael El-Deredy

  • A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients

    Daniele Soria;Jonathan M. Garibaldi;Federico Ambrogi;Andrew R. Green

  • Special Issue on Neural Networks

    P.J.G. Lisboa

  • Data Mining in Cancer Research [Application Notes]

    P.J.G. Lisboa;A. Vellido;R. Tagliaferri;F. Napolitano

  • Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination

    P.J.G. Lisboa;T.A. Etchells;I.H. Jarman;C.T.C. Arsene

  • Complete solution of the local minima in the XOR problem

    P J G Lisboa;S J Perantonis

Frequent Co-Authors

Wael El-Deredy
Wael El-Deredy Valparaiso University
Stavros J. Perantonis
Stavros J. Perantonis Demokritos National Centre for Scientific Research
Jonathan M. Garibaldi
Jonathan M. Garibaldi University of Nottingham
Enrique Romero
Enrique Romero Universitat Politècnica de Catalunya
Mark A Bellis
Mark A Bellis Liverpool John Moores University
Francis McGlone
Francis McGlone Liverpool John Moores University
Ivan K. Baldry
Ivan K. Baldry Liverpool John Moores University
Chris A. Collins
Chris A. Collins Liverpool John Moores University
Thomas Villmann
Thomas Villmann Hochschule Mittweida

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