Expertise

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; - Study 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

Dr. Wim Cuypers is a postdoctoral researcher at the Adrem Data Lab at the University of Antwerp. With a strong background in microbial genomics, transcriptomics and general bioinformatics, his research focuses on employing cutting-edge techniques to understand and combat infectious diseases. Within the branch of the Adrem Data Lab headed by prof. Kris Laukens, Dr. Cuypers' principal objective centers around harnessing the revolutionary capabilities inherent to nanopore sequencing technology for the purpose of pathogen monitoring. After securing a competitive FWO-SB research grant, Wim conducted extensive investigations into microbial genomics, antimicrobial resistance and transcriptomics for his PhD research from 2018 onwards. Key contributions he made during his PhD include a review on fluoroquinolone resistance in Salmonella which has been cited over 100 times after publication in Microbial Genomics, and a large collaborative study on Salmonella Concord, which was published in Nature Communications. In 2023, he defended his PhD thesis entitled: “Genomic adaptation of Salmonella to antimicrobials and the human host”. In addition to his computational expertise, Wim is also proficient in various wet-lab techniques, including cultivating bacteria, performing DNA extractions, and performing library preps fof sequencing. This practical knowledge allows him to seamlessly integrate benchwork experiments with computational analyses, facilitating a holistic and comprehensive approach to research. Through a strong connection with the Institute of Tropical Medicine in Antwerp were he performed part of his doctoral studies, Wim has established a robust partnership that greatly enriches his academic pursuits. His diverse background and scientific network allows him to actively engage with interdisciplinary teams, fostering an environment conducive to inclusive research. Driven by a passion for effective communication, he can explain difficult scientific concepts to audiences with varying levels of technical expertise. As a true evangelist for the field of bioinformatics and computational biology, Wim holds the position of Vice Chair in the Executive Team of the International Society for Computational Biology (ISCB) Student Council. He plays a crucial role in fostering connections and building bridges within a diverse global network of more than 2000 master's students, PhD students, and early-career researchers who share a profound dedication to advancing the frontiers of computational biology. Driven by a strong sense of social responsibility, and through his active involvement in the ISCB Student Council and his current research position, Wim is dedicated to providing equitable access to sequencing capacity and high-quality bioinformatics training, particularly for individuals in low- and middle-income countries who face limited opportunities in this field.

Technique

DNA isolation Whole-genome sequencing Nanopore sequencing Sequencing QC Genome assembly Variant calling Phylogenetics Identification of resistance markers Bioinformatics pipeline development Antimicrobial susceptibility testing Cultivation of bacteria

Users

Fundamental and applied researchers and clinicians

Keywords

Sequencing, Bioinformatics, Microbiology, Bacterial resistance, Nanopore sequencing, Computational biology, Microbial genetics, Salmonella

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 mining, Machine learning, Data science, 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, Technology transfer, Metabolomics, Microbiome, Food analysis

Development and study of advanced methods for data storage, cleaning, processing and querying of vast amounts of data.

Technique

Database Systems, Data Cleaning Methods, Provenance techniques, Indexing methods.

Users

Big data analysts and users of data managements systems.

Keywords

Data providers, Theoretical study, Information technology

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

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

Both biological sciences and clinical medicine are currently overwhelmed by vast amounts of complex data and are becoming increasingly dependent on information technology for data analysis, interpretation and organisation. Although powerful data mining techniques are being developed within and outside the University, they are still underutilized in the life sciences. We aim to bring state-of-the-art data mining techniques to life science applications by setting up interdisciplinary collaborations between computer scientists, life scientists and clinicians. Core activities are: 1) the introduction, adaptation and application of innovative pattern mining and machine learning techniques to heterogeneous 'omics data (genome, transcriptome, proteome and metabolome) and to clinical information; 2) using these techniques to generate computational (network) models for biological systems and diseases; and 3) the development of interactive and intuitive visualizations of complex life science data and pattern mining results.

Technique

Various bioinformatics, data mining and artificial intelligence techniques: analysis, interpretation, pattern mining, integration of heterogeneous 'omics data (genome, transcriptome, proteome, metabolome), sequence and structure analysis, spectrum analysis and interpretation, functional analysis of molecular data, multivariate statistics, network theory, algorithmic modeling, advanced machine learning, deep learning, advanced data visualization, prototyping

Users

Life scientists, biomedical researchers, clinicians, molecular biologists

Keywords

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

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

Bioinformatics, Machine learning, Metabolomics