Cytokine-related genes identified from the RIKEN full-length mouse cDNA data set

To identify novel cytokine-related genes, we searched the set of 60,770 annotated RIKEN mouse cDNA clones (FANTOM2 clones), using keywords such as cytokine itself or cytokine names (such as interferon, interleukin, epidermal growth factor, fibroblast growth factor, and transforming growth factor). This search produced 108 known cytokines and cytokine-related products such as cytokine receptors, cytokine-associated genes, or their products (enhancers, accessory proteins, cytokine-induced genes). We found 15 clusters of FANTOM2 clones that are candidates for novel cytokine-related genes. These encoded products with strong sequence similarity to guanylate-binding protein (GBP-5), interleukin-1 receptor-associated kinase 2 (IRAK-2), interleukin 20 receptor alpha isoform 3, a member of the interferon-inducible proteins of the Ifi 200 cluster, four members of the membrane-associated family 1-8 of interferon-inducible proteins, one p27-like protein, and a hypothetical protein containing a Toll/Interleukin receptor domain. All four clones representing novel candidates of gene products from the family contain a novel highly conserved cross-species domain. Clones similar to growth factor-related products included transforming growth factor beta-inducible early growth response protein 2 (TIEG-2), TGFbeta-induced factor 2, integrin beta-like 1, latent TGF-binding protein 4S, and FGF receptor 4B. We performed a detailed sequence analysis of the candidate novel genes to elucidate their likely functional properties.

Asymmetric page split generalized index search trees for formal concept analysis

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.