A useful resource to find gene trap and enhancer trap fish lines that express GFP and Gal4FF in desired patterns, and to find insertions of the gene trap and enhancer trap constructs that are located within or near genes of interest. These transgenic fish can be utilized to observe specific cell types during embryogenesis, to manipulate their functions, and to discover novel genes and cis-regulatory elements. Therefore, zTrap facilitates studies on genomics, developmental biology and neurobiology utilizing the transgenic zebrafish resource.
Gives access to a database of periodic structures of curves on scallop shells. PyBase utilizes image processing analytical methods to classify the data. Bifurcation points, crossover points of the growth rings and ribs and connected lines constitutes the patterns of periodic structures. Species identification is enabled by the individual recognition method. The database permits multiscale segmentation, identification cyclic structures and pattern matching.
Aims to facilitate the learning of issues in cardiovascular risk scoring. cvdRiskData consists of a synthetic dataset and curricular materials modeled on a real patient cohort that includes clinical and genetic covariates. The script, Bayesian network, and conditional probability tables (CPTs) used to generate the dataset are also included. The dataset can be used for (1) teaching math, statistics, (2) teaching machine learning techniques to biologists and clinicians, and (3) assessing the performance of machine learning techniques on a tunable dataset.
Serves as a repository of datasets of several real-life experiments and can be used as biological imaging benchmark tool. IICBU’s main purpose is to provide a tool for testing and comparing the performance of image analysis algorithms for biological imaging. The image datasets collected on this repository represent a broad range of biological imaging problems and are based on actual biological experiments.
Gathers Ziehl-Neelsen (ZN) sputum smear microscopy image data. ZNSM-iDB includes 7 categories of different datasets collected from three bright-field microscopes. This database serves as a tool for developing algorithm on several domains: (i) autofocusing of a view field; (ii) autostitching of view fields to get panoramic view of ZN stained slide and (iii) identification and grading of Mycobacterium tuberculosis bacilli for automatic detection of tuberculosis.
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