DETEXA: declarative extensible text exploration and analysis through SQL.

Metadata enrichment through text mining techniques is becoming one of the most significant tasks in digital libraries. Due to the exponential increase of open access publications, several new challenges have emerged. Raw data are usually big, unstructured, and come from heterogeneous data sources.

MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU.

Complementary DNA (cDNA) microarray is a powerful tool for simultaneously studying the expression level of thousands of genes. Nevertheless, the analysis of microarray images remains an arduous and challenging task due to the poor quality of the images that often suffer from noise, artifacts, and uneven background. In this study, the MIGS-GPU [Microarray Image Gridding and Segmentation on Graphics Processing Unit (GPU)] software for gridding and segmenting microarray images is presented.

Identification of environmental chemicals that induce yolk malabsorption in zebrafish using automated image segmentation.

Environmental factors affecting nutrient availability during development can cause predisposition to diseases later in life. To identify chemicals in the environment capable of altering nutrient mobilization, we analyzed yolk malabsorption in the zebrafish embryo, which relies on maternally-derived yolk for nutrition during its first week of life. Embryos of the transgenic zebrafish line HGn50D, which fluoresce in the yolk syncytial layer, were exposed from two to five days post fertilization to different chemicals.

Grow-cut based automatic cDNA microarray image segmentation.

Complementary DNA (cDNA) microarray is a well-established tool for simultaneously studying the expression level of thousands of genes. Segmentation of microarray images is one of the main stages in a microarray experiment. However, it remains an arduous and challenging task due to the poor quality of images.

An effective approach for detection and segmentation of protein spots on 2-D gel images.

Two-dimensional gel image analysis is widely recognized as a particularly challenging and arduous process in proteomics field. The detection and segmentation of protein spots are two significant stages of this process as they can considerably affect the final biological conclusions of a proteomic experiment. The available techniques and commercial software packages deal with the existing challenges of 2-D gel images in a different degree of success.

Segmentation of zebrafish embryonic images using a geometric atlas deformation.

Transgenic zebrafish expressing fluorescent proteins in specific tissues or organs are promising models for studies of normal developmental processes, or perturbations of these. However, for widespread use, reliable quantification of the observed effects is necessary. Therefore, accurate and automatic analysis of images obtained by fluorescent microscopy and depicting zebrafish embryos becomes crucial.

Automatic segmentation of time-lapse microscopy images depicting a live Dharma embryo.

Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish Dharma embryo can be greatly affected by the analysis of the time-lapse microscopy images depicting the embryo. Among the stages of image analysis, automatic and accurate segmentation of the Dharma embryo is the most crucial and challenging. In this paper, an accurate and automatic segmentation approach for the segmentation of the Dharma embryo data obtained by fluorescent time-lapse microscopy is proposed.

3-D spot modeling for automatic segmentation of cDNA microarray images.

Spot segmentation--the second essential stage of cDNA microarray image analysis--constitutes a challenging process. At present, several up-to-date spot-segmentation techniques or software programs--proposed in the literature--are often characterized as "automatic." On the contrary, they are in effect not fully automatic since they require human intervention in order to specify mandatory input parameters or to correct their results.

An original genetic approach to the fully automatic gridding of microarray images.

Gridding microarray images remains, at present, a major bottleneck. It requires human intervention which causes variations of the gene expression results. In this paper, an original and fully automatic approach for accurately locating a distorted grid structure in a microarray image is presented.