Pattern Recognition and Bioinformatics

The Pattern Recognition & Bioinformatics Section is an organizational section focussed on pattern recognition and its applications to computer vision and bioinformatics.

Research area

Pattern recognition is concerned with processing raw measurement data by a computer to arrive at a prediction, which can then be used to formulate a decision or action to take. Problems to which pattern recognition are applied have in common that they are too complex to model explicitly, thus requiring algorithms to learn parameters in generic models from limited sets of examples. Pattern recognition practice is firmly focused on real-world, sensor-based applications. This places it at the core of the current process of scientific discovery, by allowing researchers to derive regularities in large amounts of data in areas as diverse as physics, biology and geology, but also psychology and neuroscience. Pattern recognition algorithms also find application in industrial and consumer settings, allowing machines to sense the environment and to decide on actions or support human decision making. The PRB section studies both aspects in three different research labs. One research lab (the pattern recognition laboratory) focuses on the foundations of pattern recognition: representation and generalization, in which new ways of describing objects and learning from examples are studied. Two other research labs (the vision lab and the bioinformatics lab) apply these techniques in the domains of images and of molecular biology.

Research activities & vision

Pattern Recognition Laboratory

The Pattern Recognition Laboratory is concerned with the classical trinity of representation, generalization, and evaluation: the core elements of every pattern recognition system. The principal focus is on developing tools and theories and gaining knowledge and understanding applicable to a broad range of general problems.

Delft Bioinformatics Lab

The Delft Bioinformatics Lab deals with developing novel computer models and algorithms to further fundamental biological knowledge and apply these models and algorithms to advance the state-of-the-art in health care and industry. The lab focuses on data-driven bioinformatics: creating algorithms to infer and exploit simple models of complex interactions, by coupling biological insights and available prior knowledge to high-throughput measurements. Recent contributions include the discovery of novel cancer genes by analyzing and modeling insertional mutagenesis data; proposing (combinatorial) cultivation dependent transcription factor activities based on a decomposition of transcriptomics data; and assessing gene therapy protocols by integrating viral insertions with gene expression data. Future prominent areas include studying robustness in microbial systems, application of scale-space theory to detect functional modules in molecular data at different levels, analyzing same-sample multi-modal molecular data to infer combinatorial interactions between different components, and building atlases for both local sequence variations and gross structural variants during evolution experiments.

Vision Lab

The Vision Lab focuses on the analysis of multidimensional sensor data (such as images, image sequences, multiple cameras, 3D/4D medical data like MRI and CT, and other ubiquitous sensing modalities). With the breakthrough in deep learning we are interested in motion-representation learning, knowledge-based deep learning, deep learning with few examples. Example applications include, but are not limited to activity analysis, video indexing, medical imaging, human behaviour analysis, and anomaly detection.

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