Ricardo J. Pais

About

Ricardo J. Pais is a senior bioinformatician and data scientist that works in the UK biomedical industry. Ricardo holds a PhD degree in Systems biology, gathering more than 15 years of experience in the field of bioinformatics, data science and mathematical modelling of physiological systems. His work is focused on the development of algorithms, software tools and mathematical models with medical applications for disease diagnostics, prognostics and personalized medicine. Ricardo is an active researcher and an invited professor of bioinformatics at EGAS MONIZ SCHOOL OF HEALTH & SCIENCE (Portugal). Currently, he also is the head of R&D in the bioinformatics department of MOLMART LTD and the director of Bioenhancer Systems LTD.

Employment

• MOLMART LTD Head of R&D
  2022 - 2026
• BIOENHANCER SYSTEMS LTD Director
  2020 - Present
• MAP Sciences Lda Principal Data Scientist
  2018 - 2020
• Instituto Gulbenkian de Ciencia PhD student
  2013 - 2017
• Centro de Neurociências e Biologia Celular de Coimbra Finding the naturally evolved design principles of prevalent metabolic circuits
  2011 - 2012
• Faculdade de Ciências de Lisboa Reconstruction and systems analysis of the reaction networks of reactive oxygen, nitrogen and sulfur species in representative physiological systems
  2009 - 2011
• Faculdade de Ciências e Tecnologia Cloning, overexpression and purification of rubredoxins. Biochemical substitution of metal in rubredoxins active sites and characterization by UV-Vis, EPR and cyclic voltammetry.
  2005 - 2006

Education

• Universidade Nova de Lisboa Instituto de Tecnologia Química e Biológica Ph.D. in Systems Biology
  2013 - 2018
• Universidade de Lisboa Faculdade de Ciencias Master science in Medical biochemistry
  2009 - 2011
• Universidade de Lisboa Faculdade de Ciencias Bachelor in Biochemistry
  2003 - 2008

Projects & Funding

Projects & funding information is unavailable.

Publications (32)

• Bespoke Biomarker Combinations for Cancer Survival Prognosis Using Artificial Intelligence on Tumour Transcriptomics
  2025 DOI: 10.3390/msf2025037018
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• Visualising the Truth: A Composite Evaluation Framework for Score-Based Predictive Models Selection
  2025 DOI: 10.20944/preprints202506.1803.v1
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• Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility Study
  Diagnostics 2025 DOI: 10.3390/diagnostics15040425
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• Exploring AI-Driven Machine Learning Approaches for Optimal Classification of Peri-Implantitis Based on Oral Microbiome Data: A Feasibility Study
  2025 DOI: 10.20944/preprints202501.0529.v1
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• Linking peri-implantitis to microbiome changes in affected implants, healthy implants, and saliva: a cross-sectional pilot study
  Frontiers in Cellular and Infection Microbiology 2025 DOI: 10.3389/fcimb.2025.1543100
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• Visualising the Truth: A Composite Evaluation Framework for Score-Based Predictive Model Selection
  BioMedInformatics 2025 DOI: 10.3390/biomedinformatics5030055
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• INSaFLU-TELEVIR: an open web-based bioinformatics suite for viral metagenomic detection and routine genomic surveillance
  Genome Medicine 2024 DOI: 10.1186/s13073-024-01334-3
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• Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach
  2023 DOI: 10.3390/msf2023022006
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• Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics
  BioMedInformatics 2023 DOI: 10.3390/biomedinformatics3040064
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• Facilitating NGS-Based Screening of Genetic Disorders Using -AI-Driven Bioinformatics
  The 6th International Congress of CiiEM—Immediate and Future Challenges to Foster One Health 2023 DOI: 10.3390/msf2023022039
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