The research activities of the group focus on innovative interactive visualisation approaches to support machine learning for big data by tackling open research questions in two novel research areas: Interactive Machine Learning (IML) and Visual Analytics (VA). Both areas rely on human knowledge to improve the learning systems. IML focuses on the development of machine learning procedures based on design choices such as selection and creation of the model, definition of evidential features, and the setting of parameters.

Information Retrieval (IR) is concerned with complex systems delivering a variety of key applications to industry and society: Web search engines, (bio)medical search, expertise retrieval systems, intellectual property and patent search, enterprise search, and many others.

The advent of the widespread digitization of cultural heritage collections has significant implications for institutions that hold these types of collections. Both digital preservation and access present challenges for owners of cultural heritage collections. The issues that surround access are complex and far-reaching.

• Multivariate selection of genetic markers in complex diseases

• Prediction of disease evolution based on genetic and phenotypic markers

• Compression and fast retrieval of genome-wide genetic variation data

People: Silvana Badaloni (contact person)

Next-generation sequencing technologies allow the collection of massive amounts of genomic measurements, including somatic mutations, in large cohorts of cancer patients. The analysis of these massive amounts of data poses many computational challenges and requires the design of efficient and rigorous algorithmic techniques.

Modern sequencing technologies generate data more efficiently, economically, and with greater depth than previously possible. This has fostered a number of sequencing-based applications like genome re-sequencing, RNA-Seq, ChIP-Seq etc. However the data volume generated is growing at a pace that is now challenging the storage and data processing capacities of modern computer systems. In particular, core research activities in the field are:

With thousands of genomes made available by next-generation sequencing technologies, one of the core challenges for bioinformaticians is how to analyze and compare them on a large scale.  Within this context it is essential to develop efficient algorithms and tools that are capable of dealing with whole genomes representations as long sequences or huge sets of reads using appropriate data structures and combinatorial pattern matching techniques. Current research includes:

• Models of integration of quantitative and qualitative imperfect temporal information, based on the FCSP (Fuzzy Constraint Satisfaction Problem) paradigm;

• Development of automated systems capable of representing and reasoning about temporal knowledge in presence of uncertainty and vagueness; application to planning and scheduling problems and to medical diagnosis;