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. In this paper, we introduce a text analysis framework implemented in extended SQL that exploits the scalability characteristics of modern database management systems. The purpose of this framework is to provide the opportunity to build performant end-to-end text mining pipelines which include data harvesting, cleaning, processing, and text analysis at once. SQL is selected due to its declarative nature which offers fast experimentation and the ability to build APIs so that domain experts can edit text mining workflows via easy-to-use graphical interfaces. Our experimental analysis demonstrates that the proposed framework is very effective and achieves significant speedup, up to three times faster, in common use cases compared to other popular approaches.

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. MIGS-GPU's computations are performed on the GPU by means of the compute unified device architecture (CUDA) in order to achieve fast performance and increase the utilization of available system resources. Evaluation on both real and synthetic cDNA microarray images showed that MIGS-GPU provides better performance than state-of-the-art alternatives, while the proposed GPU implementation achieves significantly lower computational times compared to the respective CPU approaches. Consequently, MIGS-GPU can be an advantageous and useful tool for biomedical laboratories, offering a user-friendly interface that requires minimum input in order to run.

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. We developed a software package to automatically and accurately segment and quantify the area of the fluorescing yolk in images captured at the end of the treatment period. Based on this quantification, we found that prochloraz decreased yolk absorption, while butralin, tetrabromobisphenol A, tetrachlorobisphenol A and tributyltin increased yolk absorption. Given the number and variety of industrial chemicals in commerce today, development of automated image processing to perform high-speed quantitative analysis of biological effects is an important step for enabling high throughput screening to identify chemicals altering nutrient absorption.

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. Images suffer from noise, artifacts, and uneven background, while spots depicted on images can be poorly contrasted and deformed. In this paper, an original approach for the segmentation of cDNA microarray images is proposed. First, a preprocessing stage is applied in order to reduce the noise levels of the microarray image. Then, the grow-cut algorithm is applied separately to each spot location, employing an automated seed selection procedure, in order to locate the pixels belonging to spots. Application on datasets containing synthetic and real microarray images shows that the proposed algorithm performs better than other previously proposed methods. Moreover, in order to exploit the independence of the segmentation task for each separate spot location, both a multithreaded CPU and a graphics processing unit (GPU) implementation were evaluated.

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. Furthermore, they require extensive human intervention which not only limits the throughput but unavoidably questions the objectivity and reproducibility of results. This paper introduces a novel approach for the detection and segmentation of protein spots on 2-D gel images. The proposed approach is based on 2-D image histograms as well as on 3-D spots morphology. It is automatic and capable to deal with the most common deficiencies of existing software programs and techniques in an effective manner. Experimental evaluation includes tests on several real and synthetic 2-D gel images produced by different technology setups, containing a total of ∼ 21,400 spots. Furthermore, the proposed approach has been compared with two commercial software packages as well as with two state-of-the-art techniques. Results have demonstrated the effectiveness of the proposed approach and its superiority against compared software packages and techniques.

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. In this paper, a segmentation approach for such images is proposed. The segmentation is achieved by fitting a species-specific 2D atlas to the zebrafish data depicted in the images. Experiments performed in a set of 50 images have provided promising results.

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. Experiments performed in four stacks of 3D images over time have shown promising results.

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. Human intervention, however, can inevitably modify the actual results of the cDNA microarray experiment and lead to erroneous biological conclusions. Therefore, the development of an automated spot-segmentation process becomes of exceptional interest. In this paper, an original and fully automatic approach to accurately segmenting the spots in a cDNA microarray image is presented. In order for the segmentation to be accomplished, each real spot of the cDNA microarray image is represented in a three-dimensional (3-D) space by a 3-D spot model. Each 3-D spot model is determined via an optimization problem, which is solved by using a genetic algorithm. The segmentation of real spots is conducted by drawing the contours of their 3-D spot models. The proposed method has been compared with various published and established techniques, using several synthetic and real cDNA microarray images that contain thousands of spots. The outcome has shown that the proposed method outperforms prevalent existing techniques. It is also noise resistant and yields excellent results even under adverse conditions such as the appearance of spots of various sizes and shapes.

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. The gridding process is expressed as an optimization problem which is solved by using a genetic algorithm (GA). The GA determines the line-segments constituting the grid structure. The proposed method has been compared with existing software tools as well as with a recently published technique. For this purpose, several real and artificial microarray images containing more than one million spots have been used. The outcome has shown that the accuracy of the proposed method achieves the high value of 94% and it outperforms the existing approaches. It is also noise-resistant and yields excellent results even under adverse conditions such as arbitrary grid rotations, and the appearance of various spot sizes.