Multimodal entity linking (MEL) is the task that consists of matching textual and visual mentions of entities in unstructured data to their corresponding entities in a knowledge base (KB). To be effective in large-scale practical settings, MEL systems must meet three objectives: high linking accuracy, computational efficiency, and storage efficiency, i.e., a compact yet efficient index of the KB. In this paper, we highlight that state-of-the-art systems fail to simultaneously satisfy these 3 requirements. To meet this three-fold objective, we propose FAST-MEL, a lightweight encoder-based MEL solution that relies on a novel and compact fixed-size vectorized representation of both the textual and visual information of each entity or mention. It matches the accuracy of the best systems but performs three orders of magnitude faster. It also consumes one order of magnitude less storage than the fastest systems.

UNLABELLED: AutoGRAPH is an interactive web server for automatic multi-species comparative genomics analyses based on personal datasets or pre-inserted public datasets. This program automatically identifies conserved segments (CS) and breakpoint regions, assesses the conservation of marker/gene order between organisms, constructs synteny maps for two to three species and generates high-quality, interactive displays facilitating the identification of chromosomal rearrangements. AutoGRAPH can also be used for the integration and comparison of several types of genomic resources (meiotic maps, radiation hybrid maps and genome sequences) for a single species, making AutoGRAPH a versatile tool for comparative genomics analysis. AVAILABILITY: http://genoweb.univ-rennes1.fr/tom_dog/AutoGRAPH/. SUPPLEMENTARY INFORMATION: A description of the algorithm and additional information are available at http://genoweb.univ-rennes1.fr/tom_dog/AutoGRAPH/Tutorial.php.

MOTIVATION: Genome maps are fundamental to the study of an organism and essential in the process of genome sequencing which in turn provides the ultimate map of the genome. The increased number of genomes being sequenced offers new opportunities for the mapping of closely related organisms. We propose here an algorithmic formalization of a genome comparison approach to marker ordering. RESULTS: In order to integrate a comparative mapping approach in the algorithmic process of map construction and selection, we propose to extend the usual statistical model describing the experimental data, here radiation hybrids (RH) data, in a statistical framework that models additionally the evolutionary relationships between a proposed map and a reference map: an existing map of the corresponding orthologous genes or markers in a closely related organism. This has concretely the effect of exploiting, in the process of map selection, the information of marker adjacencies in the related genome when the information provided by the experimental data is not conclusive for the purpose of ordering. In order to compute efficiently the map, we proceed to a reduction of the maximum likelihood estimation to the Traveling Salesman Problem. Experiments on simulated RH datasets as well as on a real RH dataset from the canine RH project show that maps produced using the likelihood defined by the new model are significantly better than maps built using the traditional RH model. AVAILABILITY: The comparative mapping approach is available in the last version of de Givry,S. et al. [(2004) Bioinformatics, 21, 1703-1704, www.inra.fr/mia/T/CarthaGene], a free (the LKH part is free for academic use only) mapping software in C++, including LKH (Helsgaun,K. (2000) Eur. J. Oper. Res., 126, 106-130, www.dat.ruc.dk/keld/research/LKH) for maximum likelihood computation.

UNLABELLED: CRH_Server is an on line Comparative and Radiation Hybrid mapping Server dedicated to canine genomics. CRH_Server allows users to compute their own RH data using the current canine RH map, and allows comparative dog/human mapping analyses. Finally, it suggests multiple options for storage and queries of the dog RH database. AVAILABILITY: http://idefix.univ-rennes1.fr:8080/Dogs/rh-server.html. SUPPLEMENTARY INFORMATION: All information is available at http://idefix.univ-rennes1.fr:8080/Dogs/help_rh-server.html.