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Abstract The objectives were to describe the morphometric measurements and determine the best model for estimating the relationship between body weight and morphometric measurements of the two Sudanese antelopes, Dorcas Gazelle (Gazella dorcas) and Bohor Reedbuck (Redunca redunca). Twenty-four animals belonging to two Sudanese antelope species, six males and six females from each species were used. Data on body weight and body measurements were recorded from each species. Averages of body weight and morphometric traits of Bohor Reedbuck were larger than the corresponding values of Dorcas Gazelle, while the opposite was true in the case of horn thickness, which was larger in the later. Stepwise regression analysis indicated that, the best model for Dorcas Gazelle had the variables neck length, belly girth and chest height, while Bohor Reedbuck had body length, head length, neck length, tail length, chest girth and pelvic height. These variables explained 82% of the total variation in body weight of Dorcas Gazelle, and 92% of the variation of Bohor Reedbuck. These results are discussed in relation to morphometric measurements reported for antelopes elsewhere.

Text mining has become an important tool in bioinformatics research with the massive growth in the biomedical literature over the past decade. Mining the biomedical literature has resulted in an incredible number of computational algorithms that assist many bioinformatics researchers. In this paper, we present a text mining system called Gene Interaction Rare Event Miner (GIREM) that constructs gene-gene-interaction networks for human genome using information extracted from biomedical literature. GIREM identifies functionally related genes based on their co-occurrences in the abstracts of biomedical literature. For a given gene g, GIREM first extracts the set of genes found within the abstracts of biomedical literature associated with g. GIREM aims at enhancing biological text mining approaches by identifying the semantic relationship between each co-occurrence of a pair of genes in abstracts using the syntactic structures of sentences and linguistics theories. It uses a supervised learning algorithm, weighted logistic regression to label pairs of genes to related or un-related classes, and to reflect the population proportion using smaller samples. We evaluated GIREM by comparing it experimentally with other well-known approaches and a protein-protein interactions database. Results showed marked improvement.