Abdominal aortic aneurysm (AAA) is a common disease affecting elderly people and increasing in incidence. The most feared complication of AAA is the rupture of which most will result in death. The AAA involves the exc...
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Abdominal aortic aneurysm (AAA) is a common disease affecting elderly people and increasing in incidence. The most feared complication of AAA is the rupture of which most will result in death. The AAA involves the excessive dilation of the abdominal aorta in diameter. As a result, open surgery or endoluminal repair is indicated in AAA greater than 55 mm. Currently screening and assessment of AAA can be achieved by either ultrasound or computed tomography (CT) angiography, where the latter imaging technology is the current gold standard. Each AAA is different having varying percentage of thrombus, total volume, luminal volume and calcification all of which are thought to play a critical role for assessing the rupture risk and determining management. Currently measurement of these parameters is based on manual or semi-automatic CT image segmentation - it is time-consuming, inaccurate and becomes unrealistic in clinical practice. The development of an automated method for the segmentation of AAA CT images is therefore demanding. We introduce in this paper a geostatistically constrained fuzzy c -means based algorithm as an automatic and effective segmentation of such images.
The mouse genome informatics (MGI) resource, an in-depth resource for the genetics, genomics and biology of the laboratory mouse, provides free access to integrated data on diverse biological attributes, ranging from ...
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The mouse genome informatics (MGI) resource, an in-depth resource for the genetics, genomics and biology of the laboratory mouse, provides free access to integrated data on diverse biological attributes, ranging from sequence to phenotype and disease model representation. MGI advances translational research through an integrated data platform that facilitates acquisition, explicit representation, and semantic querying and interpretation of multi-parametric genome-scale datasets, and fosters interoperability across different model organism systems and disparate data sources. To this end, MGI employs a set of logically rigorous, dynamic, and cross-referenceable ontologies to unambiguously describe current biological knowledge, expedite manual curation, and advance the informatics capacity to execute complex data mining tasks relevant to comparative and functional genomics. Major bio-ontologies developed and implemented at MGI include the gene ontology (GO), Mammalian Phenotype (MP) ontology, and adult mouse anatomical (MA) Dictionary, reviewed in this paper. All these share a common generic vocabulary infrastructure, and utilize identical annotation tools and web-based browsers to reinforce ontology-centric curation and support ontology-driven querying of the vocabularies and the associated knowledgebase.
Cancer is a group of complex diseases, in which a relatively large number of genes are involved. One of the main goals of cancer research is to identify genes that causally relevant to the development and progress of ...
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Cancer is a group of complex diseases, in which a relatively large number of genes are involved. One of the main goals of cancer research is to identify genes that causally relevant to the development and progress of cancer. The increasingly identified cancer genes and availability of genomic and proteomics data provide us opportunities to identify cancer genes by computational methods. In this work, we investigated five predictive topological features, derived from the protein-protein interaction networks, in identifying cancer genes. We used 10-fold cross validation to assess the predictive ability of all the combinations of these features and found the most predictive feature and feature combinations. Two kinds of neural networks, support vector machine (SVM) and multi-layer perceptrons (MLP), were employed to assess the predictive ability of features. We found that the best feature combination for these two algorithms is the same. At the same time, we found SVM performs slightly better than MLP. Using only 2 or 3 features, the best performance of our classification model can get accuracy as high as 73.9%.
Grid computing technology is able to integrate and share large-scale distributed computation and data resource to facilitate the scientific researches. Recently, the grid workflow support and large-scale distributed d...
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Grid computing technology is able to integrate and share large-scale distributed computation and data resource to facilitate the scientific researches. Recently, the grid workflow support and large-scale distributed data management are becoming two main requirements of scientists and researchers in many fields, such as bioinformatics, high-energy physics etc. In this paper, we proposed to support grid workflow for data intensive applications using CSF4 scheduling plug-ins. The grid workflow scheduling and data aware scheduling policies are implemented in two scheduling plug-ins, grid workflow plug-in and grid data aware plug-in, respectively. The two scheduling plug-ins can work together smoothly. The data aware plug-in will automatically dispatch the workflow tasks to the grid sites which are close to data replicas. At last, the experiment results are given to show the improvement of system performance and optimization of scheduling.
The following topics were dealt with: biological data mining;high performance biocomputing; comparative genomics; biological databases; data integration; data visualization; molecular sequence analysis; healthcare inf...
The following topics were dealt with: biological data mining;high performance biocomputing; comparative genomics; biological databases; data integration; data visualization; molecular sequence analysis; healthcare informatics; computational proteomics; protein structure; computationalsystems biology; microarray design; ontologies; gene networks; biomedical signal processing; biomedical image processing; biomedical information systems; and intelligent biomedical knowledge discovery.
The proceedings contain 160 papers from the 2004 ieee computational systems bioinformatics conference, CSB 2004. The topics discussed include: fractal genomics modeling: a new approach to genomic analysis and biomarke...
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ISBN:
(纸本)0769521940
The proceedings contain 160 papers from the 2004 ieee computational systems bioinformatics conference, CSB 2004. The topics discussed include: fractal genomics modeling: a new approach to genomic analysis and biomarker discovery;from DNA sequence to network behavior: functional properties of genetic regulatory networks;segmental duplications containing tandem repeated genes encoding putative deubiquitinating enzymes;profile-based string kernels for remote homology detection and motif extraction;a mixed factors model for dimension reduction and extraction of a group structure in gene expression data;improved Fourier transform method for unsupervised cell-cycle regulated gene prediction;and biclustering in gene expression data by tendency.
The growing size of sequence, protein and other biological databases results in an increased computational complexity of the analysis process. Often parallelization is the only solution to limit the turnaround time wi...
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We investigated how to establish a realistic model for the evolutionary emergence of gene network that is scale-free. After theoretical analysis and compute simulation, we have demonstrated the evolutionary principles...
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Alternative splicing is a highly important process in many eukaryotic organisms, but surprisingly little is known about its regulation. Often, this process involves cis-regulatory DNA motifs located within the alterna...
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