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I conduct bioinformatics research in mass spectrometry-based proteomics, aimed at supporting the study of protein composition in biological samples or tissues. I develop computational methods to improve the analysis of difficult-to-interpret mass spectrometry data. My focus is on the annotation of post-translational modifications and immunopeptides, which are particularly relevant in research on infectious diseases and cancer. In addition, I explore the ethical aspects of mass spectrometry data, especially as this technology is increasingly used in clinical settings.

Technique

I apply various bioinformatics and data analysis techniques for processing and interpreting mass spectrometry-based proteomics data. My expertise includes machine learning, deep learning, peptide-spectrum match rescoring, and open modification searching, with the aim of improving the annotation of post-translational modifications and immunopeptides.

Users

My expertise is valuable to researchers in the life sciences, particularly those working with mass spectrometry-based proteomics data. Biotech and pharmaceutical companies can also benefit from my methods for protein identification and data analysis. In addition, policymakers and ethicists involved in the regulation of health data and privacy in omics research may find my insights into the ethical dimensions of mass spectrometry data useful.

Keywords

Bioinformatics, Mass spectrometry, Immunopeptidomics, Proteomics

Dr. Bittremieux's research deals with developing advanced machine learning techniques to uncover novel knowledge from mass spectrometry-based proteomics and metabolomics data. While his current research mainly focuses on how deep learning can be used to analyze mass spectrometry data he is interested in a wide variety of bioinformatics problems. An important part of his work involves developing insights and computational approaches for quality control in biological mass spectrometry.

Technique

bioinformatics machine learning deep learning mass spectrometry proteomics metabolomics

Users

bioinformatics researchers computational biology researchers

Keywords

Machine learning

Analysis of dynamic network data Most works in network analysis concentrate on static graphs and find patterns such as the most in influential nodes in the network. Very few existing methods are able to deal with repeated interactions between nodes in a network. The main goal of the research in this topic is hence to fill this gap by developing methods to identify patterns in interactions between network nodes. We studied so-called information channels that indicate information flows. Process Mining In process mining the object of study are logs generated by business processes. Consider for instance a log generated by a leave request system, recording activities such as users logging in, opening a new request, managers approving requests, emails being sent by the system, etc. In process mining such logs are analyzed to better understand, monitor, and improve the business processes. One tasks in this context is detecting complex events. Complex events can be used to find pre-defined security problems or abnormalities. Often, however, anomalies may occur that are not foreseen in the systems. In order to be able to handle such cases, anomaly detection techniques are necessary. With the following work on model-based anomaly detection using dynamic Bayesian networks, we won the Business Process Intelligence challenge at the BPM 2018 conference: S. Pauwels and T. Calders. Detecting and Explaining Drifts in Yearly Grant Applications. In BPM Workshop Business Process Intelligence (BPI), 2018. Fairness-Aware Machine Learning In contemporary society we are continuously being profiled; banks have profiles to divide up people according to credit risk, insurance companies profile clients for accident risk, telephone companies profile users on their calling behavior, web corporations profile users according to their interests and preferences based on web activity and visitation patterns. These profiles are more and more built automatically by machine learning methods trained on historical data. Within society there are growing concerns that these machine learning methods do not have ethical or moral restrictions. Recent studies show indeed that in circumstances where historical data is biased, or when there is omitted variable bias, automatically learned methods may take decisions that could be considered discriminatory. Apart from ethical considerations, there are also legal restrictions to the use of profiling methods that blindly optimize accuracy without taking unwanted discriminatory effects into account. The recent General Data Protection Regulation (GDPR; Regulation (EU) 2016/679) explicitly mentions profiling (Art. 22 GDPR Automated individual decision-making, including profiling) as an activity in which decisions should not be based on personal data and suitable measures should be in place to safeguard the data subjects rights and freedoms and legitimate interests. Most profiling techniques, however, do not consider anti-discrimination legislation and may unintentionally produce models that are unfair and hence do not safeguard the data subjects freedoms. A further complication is that often detecting whether a model is unfair, is highly non-trivial.

Technique

- Formalizing research problems mathematically; - Development of algorithmic solutions; - 91³Ô¹ÏÍø of computational properties of algorithms; - Case studies: application of developed techniques in real-life contexts.

Users

All sectors in which machine learning is applied, including: - default prediction in the financial sector; - risk assessment in insurance; - outlier detection from log files for security monitoring; - predictive modelling. There are existing collaborations with insurance companies (fairness in machine leanring - building fair score models), bankin sector (default prediction), scientific institutes (BIRA: analysis of spectrograms for meteor detection),...

Keywords

Pattern mining

Ethical AI expert and Recommender System researcher.

Technique

Audits of algorithmic fairness via statistical testing. Theoretical analysis of the limits of Fair Machine Learning techniques. Studies of recommender system limitations.

Users

Companies using AI for human-dependent tasks Companies with online platforms Government

Keywords

Fairness

Development and study of advanced data mining and machine learning methods. In particular, we investigate: (i) new methods to efficiently discover interesting patterns in sequential data; (ii) new methods to detect contextual anomalies in heterogeneous sequential data; (iii) and new methods for multi-label classification in extremely large datasets. In addition, we investigate applications of these methods in areas such as the monitoring of wind farms and anomaly detection in an Industrial Internet of Things context.

Technique

Development of algorithmic solutions to (un)supervised machine learning problems; Formalizing research problems mathematically; Development of algorithmic solutions; Analysis of properties of algorithms; Case studies: application of developed techniques in real-life contexts.

Users

All sectors in which data mining or machine learning is applied. More specifically, anomaly detection, prediction and/or discovering patterns in sequential data, such as event log data and time series. There are existing collaborations for: (i) predicting labels for federal police; (ii) condition monitoring in wind turbine farms (using pattern mining); (iii) anomaly detection in water consumption of supermarket chains; (iv) data cleaning and entity resolution to combine different databases.

Keywords

Data science, Data mining, Machine learning, Pattern mining

metabolomics analysis, microbiome analyses, structural characterization

Technique

untargeted mass spectrometry; clinical trial design; technology transfer

Users

Those interested in untargeted metabolomics and the impact of diet on health at the molecular level. Those in need of technology transfer expertise.

Keywords

Nanopore sequencing, Microbiome, Food analysis, Technology transfer, Metabolomics

Machine learning methods for structured data, including graphs, tables, and relational databases, with an emphasis on fundamental/theoretical insights.

Technique

Graph Neural Networks, Transformer Architectures, Mathematical Logic, Computational Complexity Theory, Linear Algebra, and Database Theory

Users

Scientists and data analysts who work with structured data and aim to derive insights from it using machine learning methods.

Keywords

Information technology, Theoretical study, Learning algorithm, Data models

Development and study of advanced methods for Data Mining, Big Data Analytics, Recommender Systems, Data Cleaning, and other technologies related to the management and analysis of large amounts of data.

Technique

Data Mining, Big Data Analytics, Recommender Systems, Data Cleaning

Users

Analysts and managers of large amounts of data

Keywords

Mathematics, Data mining, Data quality, Informatics, Pattern mining, Data analysis

My expertise is in bioinformatics, where for more than two decades I have been developing and applying novel methods to analyze biomedical data. With a strong focus on machine learning, data mining, and artificial intelligence, I design algorithms and explainable AI models that transform complex data into actionable insights. These methodological innovations are applied across domains such as multi-omics, molecular interaction networks, immune receptor analysis, and mass spectrometry. My research spans a wide range of applications, often but not exclusively in infectious diseases, oncology, and the clinical monitoring of patients, for example in intensive care settings. Central to my work is the translation of computational advances into precision medicine, enabling biomarker discovery, the design and evaluation of immunotherapies, and the support of personalized treatments. Valorization is a key dimension of my research, ensuring that innovations flow into practice, including through spin-offs such as ImmuneWatch. In this way, I aim to foster an ecosystem where bioinformatics and artificial intelligence not only accelerate scientific discovery but also deliver tangible impact on healthcare and society.

Technique

I apply a broad range of bioinformatics, data analysis, and AI techniques to interpret biomedical data and transform them into actionable insights. This includes the integration and analysis of heterogeneous omics data (genome, transcriptome, proteome, metabolome), sequence and structure analysis of biomolecules, and spectrum interpretation in mass spectrometry. I use multivariate statistics, network and graph theory, and algorithmic modeling to unravel complex molecular interactions. In addition, I develop and apply advanced machine learning and deep learning methods, including natural language processing, generative models, and explainable AI, tailored to biomedical applications. Data mining, pattern recognition, and innovative visualization and prototyping techniques are also part of my toolbox. My methods are always designed with clinical translation in mind: not merely to build the theoretically best model, but to create solutions that can be effectively deployed in biomarker discovery, precision medicine, and patient care.

Users

This expertise is particularly relevant to researchers and clinicians working with complex biomedical data in fields such as immunology, oncology, and infectious diseases. Within Flanders and Europe, this includes academic research groups, university hospitals, and research institutes engaged in precision medicine and clinical monitoring, for example in intensive care settings. Pharmaceutical and biotechnology companies – from innovative start-ups to established players – can benefit from bioinformatics solutions for biomarker discovery, therapy development, and data integration. In addition, my work has a strong focus on valorization: with extensive experience in translating research into industrial and clinical applications, spin-offs, incubators, investors, and innovation partners are also key target groups. I also bring many years of governance experience in research and innovation bodies, making policymakers, funding agencies, and healthcare organizations equally relevant stakeholders, as they rely on expert input to shape strategies for data-driven innovation and sustainable implementation in healthcare.

Keywords

Data mining, Bioinformatics, Computational biology, Pattern mining, Systems medicine, Systems biology, Artificial intelligence (ai)

Prof. dr. Meysman specializes in the application of data mining and artificial intelligence techniques on biomedical data. He has worked on molecular 'omics data, genetic data, medical data and immunological data. He has experience with several common bioinformatics pipelines and has aided in the development of bioinformatics databases. He specializes on the processing of immunological data, in particular those related to human T-cells, in the context of ageing, vaccination, infectious disease and auto-immune disorders.

Technique

The data mining expertise includes a wide range of techniques from unsupervised methods (e.g. principle component analysis, frequent item set mining) to supervised methods (e.g. lasso regression, random forests, convolutional neural networks).

Users

Dr. Meysman has collaborated in the past with research hospitals, general practitioners, biotech and pharma companies.

Keywords

Bioinformatics, Computational biology, Immunogenicity

I work as a Postdoctoral researcher in the field of Bioinformatics, studying how to detect metabolites through Mass Spectrometry Data using Machine Learning Algorithms. For this purpose, I use public datasets in order to train Deep Learning Models that are able to predict the similarity between molecules.

Technique

I use ML techniques in order to develop models for identification of metabolites with MS/MS data.

Users

Researchers in the area of metabolomics and mass spectrometry. Scientists and engineers working on the pharmaceutic area.

Keywords

Metabolomics, Machine learning, Bioinformatics