• 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;

Preferences are a central concept of decision making. Preference models are needed in decision-support systems such as web-based recommender systems, in automated problem solvers such as configurators, and in multi-agent scenarios where we need to combine preferences given by several agents.

Natural language processing provides technologies for the analysis and processing of text documents.  These techniques can be exploited in applications such as question answering, information extraction from documents, and text summarization and translation. Core research is being conducted in the following areas:

• natural language parsing

• semantic parsing

• part-of-speech tagging

People: Giorgio Satta (contact person)

High-Performance Computing (HPC) platforms feature many processors sharing a fast communication medium and a deep memory hierarchy. Selecting a good abstract machine model for algorithm design on these platforms requires striking the right tradeoff between the largely conflicting goals of usability (ease of design and analysis), effectiveness (predictable performance on the actualÊ machines), and portability (effectiveness across machines). Some specific research themes pursued are the following: