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Adriano Barbosa da Silva
Adriano Barbosa da Silva, PhDSenior Research Associate (Postdoc)

Center for Medical Statistics, Informatics and Intelligent Systems (Institute of Artificial Intelligence and Decision Support)
Position: Research Associate (Postdoc)

ORCID: 0000-0002-5260-2607
T +43 1 40160 36333

Further Information


Artificial Intelligence; Biological Ontologies; Biostatistics; Computational Biology; Data Display; Data Interpretation, Statistical; Data Mining; Databases, Bibliographic; Databases, Genetic; Databases, Protein; Decision Making, Computer-Assisted; Epigenomics; Gene Regulatory Networks; Genomics; Medical Informatics Computing; Metabolic Networks and Pathways; Molecular Sequence Data; Programming Languages; Systems Biology; Translational Medical Research

Research group(s)

Research interests


Computational biologist with extensive accomplishments in the area of biomedical text-mining, translational bioinformatics and health data science. Scientific consultant in building customized research pipeline, integrative translational research environments based on open-source software and implementation of machine learning-based methods for data-driven diagnostic prediction.

Current Research (MedUni Wien, Center for Medical Statistics, Informatics and Intelligent Systems):

1) Intelligent Task Ontology: The Intelligence Task Ontology (ITO) provides a comprehensive map of artificial intelligence tasks, as well as broader human intelligence or hybrid human/machine intelligence tasks.
2) OpenBioLink: OpenBioLink is a resource and evaluation framework for evaluating link prediction models on heterogeneous biomedical graph data. It contains benchmark datasets as well as tools for creating custom benchmarks and training and evaluating models.

Partner Research:

3) (QMUL - UK) Machine Learning-based patients stratification of patients data (PhD thesis supervision);
4) (FIOCRUZ-RJ) Cancer severity prediction using ML;
5) (FIOCRUZ-RJ) COVID-19 single cell RNASeq data analysis for knowledge discovery;
6) (UFPE/UFMG - Brazil) Text-mining software development (LAITOR/PESCADOR);

Teaching (short workshops):

  • Introduction to Bioinformatics;
  • Machine Learning using Python/R;
  • Statistical programming using R;
  • RNA-Seq Data analysis using Bioconductor;
  • Machine learning for Epigenetics;
  • Introduction to Text-mining;
  • Systems Biology Graphical Notation (SBGN);
  • Introduction to Biological Databases;

Techniques, methods & infrastructure

  • Translational Biomedical Informatics: TranSMART, XNAT and RedCap;
  • Clincial Study Standards: CDISC (SDTM), SNOMED-CT, MedDRA;
  • Transcriptomics Data Analysis (RNA-Seq);
  • Flow Cytometry Data Analysis;
  • Mass Spectrometry Data Analysis;
  • Whole genome (re)-sequencing;
  • Variant Discovery and Genotyping;
  • Reproducible Science: Docker and Git;
  • Statistical Programming Language: R/Bioconductor;
  • Machine Learning: Python/Scikit-Learn;
  • Data Science: Pandas & Tidyverse;
  • Web-Design: HTML5/CSS/JavaScript;
  • Visual Analytics: D3 and Highcharts;
  • Web Semantics: Web services, RESTful APIs and RDF;
  • Databases: SQL, RDF and SPARQL
  • Text-mining: N.E.R., Semantic and Syntactic Analysis;
  • Network Biology: Cytoscape;
  • High Performance Computing;

Past projects:

Selected publications

  1. Piereck, B. et al., 2020. LAITOR4HPC: A text mining pipeline based on HPC for building interaction networks. BMC Bioinformatics, 21(1). Available at:
  2. Gu, W. et al., 2019. Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium. BMC Bioinformatics, 20(1). Available at:
  3. Barbosa-Silva, A. et al., 2018. Presenting and sharing clinical data using the eTRIKS Standards Master Tree for tranSMART A. Valencia, ed. Bioinformatics, 35(9), pp.1562,
  4. Satagopam, V. et al., 2016. Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases. Big Data, 4(2), pp.97,
  5. Herzinger, S. et al., 2017. SmartR: an open-source platform for interactive visual analytics for translational research data J. Hancock, ed. Bioinformatics, 33(14), pp.2229,