Multiple Sclerosis (MS), an autoimmune disorder impacting the central nervous system, is increasingly prevalent. In this context, automated tools for MS lesions segmentation could aid healthcare professionals accelerate treatment decisions and monitor disease progression. In particular, detecting small lesions may prove pivotal for early disease detection. However, current evaluation metrics such as the Dice score estimate an algorithm’s overall performance without considering its potential variability in handling lesions of diverse sizes. To address this issue, we propose an original metric, which we call ‘Dice spectrum’ that offers a more accurate estimation of the model’s performance on lesions of varying sizes. This is particularly important for early disease detection, which involves small lesions, for which the model may behave poorly despite high values of the normal Dice score. On the other hand, the Dice spectrum will clearly show the specific model’s performance on such small lesions.

Leukemia is a very heterogeneous cancer of the hematopoietic system. Since its main cause consists of genomic defects in the hematopoietic stem or progenitor cells and given the high complexity of the hematopoietic system, it may seem an important task to investigate the transcriptomic similarities and differences between leukemia subtypes and hematopoietic cells (stem cells, progenitors and differentiated cells). In this paper, we integrate the largest publicly available gene expression datasets of leukemia and normal hematopoiesis with the aim of uncovering the main gene modules involved in normal hematopoiesis as well as in the various leukemia subtypes. Using a joint consensus clustering algorithm, we have been able to relate the major leukemia types to their putative cells of origin in an unsupervised manner. While the normal hematopoietic cell modules are also active in leukemias of the corresponding cell type, our approach has determined leukemia-specific modules comprising genes with a known involvement in leukemogenesis. The expression modules uncovered implicate an unusually large number of transcription factors. This speaks against very simple models of normal hematopoiesis and leukemogenesis that involve just a handful of critical TFs, arguing for the interplay of complex transcription factor networks, in line with the findings of the FANTOM consortium for leukemia and Novershtern et al. for normal hematopoiesis.

The small sample sizes and high dimensionality of gene expression datasets pose significant problems for unsupervised subgroup discovery. While the stability of unidimensional clustering algorithms has been previously addressed, generalizing existing approaches to biclustering has proved extremely difficult. Despite these difficulties, developing a stable biclustering algorithm is essential for analyzing gene expression data, where genes tend to be co-expressed only for subsets of samples, in certain specific biological contexts, so that both gene and sample dimensions have to be taken into account simultaneously.

A decade of experience with research proposals as well as standardized query languages for the conventional Web and the recent emergence of query languages for the Semantic Web call for a reconsideration of design principles for Web and Semantic Web query languages. This chapter first argues that a new generation of versatile Web query languages is needed for solving the challenges posed by the changing Web: We call versatile those query languages able to cope with both Web and Semantic Web data expressed in any (Web or Semantic Web) markup language. This chapter further suggests that well-known referential transparency and novel answer-closedness are essential features of versatile query languages. Indeed, they allow queries to be considered like forms and answers like form-fillings in the spirit of the query-by-example paradigm. This chapter finally suggests that the decentralized and heterogeneous nature of the Web requires incomplete data specifications (or incomplete queries) and incomplete data selections (or incomplete answers); the form-like query can be specified without precise knowledge of the queried data, and answers can be restricted to contain only an excerpt of the queried data.