Ivica Slavkov

Learn More
Predictive clustering is a general framework that unifies clustering and prediction. This paper investigates how to apply this framework to cluster time series data. The resulting system, Clus-TS, constructs predictive clustering trees (PCTs) that partition a given set of time series into homogeneous clusters. In addition, PCTs provide a symbolic(More)
In biology, analyzing time course data is usually a two-step process, beginning with clustering of similar temporal profiles. After the initial clustering, depending on the expert's knowledge, descriptions of the clusters are elucidated (e.g., Gene Ontology terms that are enriched in the clusters). In this paper, we investigate the application of so-called(More)
In this paper we investigate the problem of evaluating ranked lists of biomarkers, which are typically an output of the analysis of high-throughput data. This can be a list of probes from microarray experiments, which are ordered by the strength of their correlation to a disease. Usually, the ordering of the biomarkers in the ranked lists varies a lot if(More)
  • Marianeve Carotenuto, Emilia Pedone, Donatella Diana, Pasqualino de Antonellis, Sašo Džeroski, Natascia Marino +22 others
  • 2013
Nm23-H1 is one of the most interesting candidate genes for a relevant role in Neuroblastoma pathogenesis. H-Prune is the most characterized Nm23-H1 binding partner, and its overexpression has been shown in different human cancers. Our study focuses on the role of the Nm23-H1/h-Prune protein complex in Neuroblastoma. Using NMR spectroscopy, we performed a(More)
The Kilobot is a widely used platform for investigation of swarm robotics. Physical Kilobots are slow moving and require frequent recalibration and charging , which significantly slows down the development cycle. Simulators can speed up the process of testing, exploring and hypothesis generation, but usually require time consuming and error-prone(More)
  • 1