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Find video protocols related to scientific articles indexed in Pubmed.
A novel subnetwork alignment approach predicts new components of the cell cycle regulatory apparatus in Plasmodium falciparum.
BMC Bioinformatics
PUBLISHED: 09-24-2013
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According to the World Health organization, half the worlds population is at risk of contracting malaria. They estimated that in 2010 there were 219 million cases of malaria, resulting in 660,000 deaths and an enormous economic burden on the countries where malaria is endemic. The adoption of various high-throughput genomics-based techniques by malaria researchers has meant that new avenues to the study of this disease are being explored and new targets for controlling the disease are being developed. Here, we apply a novel neighborhood subnetwork alignment approach to identify the interacting elements that help regulate the cell cycle of the malaria parasite Plasmodium falciparum.
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Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers.
BMC Genomics
PUBLISHED: 06-26-2013
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Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer.
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Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.
PLoS Comput. Biol.
PUBLISHED: 01-23-2013
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Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Coxs proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2) or L(1). This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.
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Platinum-Sensitive Recurrence in Ovarian Cancer: The Role of Tumor Microenvironment.
Front Oncol
PUBLISHED: 01-01-2013
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Despite several advances in the understanding of ovarian cancer pathobiology, in terms of driver genetic alterations in high-grade serous cancer, histologic heterogeneity of epithelial ovarian cancer, cell-of-origin for ovarian cancer, the survival rate from ovarian cancer is disappointingly low when compared to that of breast or prostate cancer. One of the factors contributing to the poor survival rate from ovarian cancer is the development of chemotherapy resistance following several rounds of chemotherapy. Although unicellular drug resistance mechanisms contribute to chemotherapy resistance, tumor microenvironment and the extracellular matrix (ECM), in particular, is emerging as a significant determinant of a tumors response to chemotherapy. In this review, we discuss the potential role of the tumor microenvironment in ovarian cancer recurrence and resistance to chemotherapy. Finally, we propose an alternative view of platinum-sensitive recurrence to describe a potential role of the ECM in the process.
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[Investigation on HBsAg carrier rate of population in schistosomiasis transmission-interrupted area of Gaoyou City, 2010].
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
PUBLISHED: 12-15-2011
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A total of 500 residents in a schistosomiasis transmission-interrupted area, Maogang Village of Gaoyou City were detected simultaneously for the infection status of schistosome and HBV, and the results showed that there was no significant difference between the HBsAg carrier rates of residents with and without the history of schistosomiasis, but the HBsAg carrier rates in some population were high, which needs more concern.
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Inferring disease and gene set associations with rank coherence in networks.
Bioinformatics
PUBLISHED: 08-08-2011
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To validate the candidate disease genes identified from high-throughput genomic studies, a necessary step is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease-causative genes are incomplete. This article introduces a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. A learning framework is proposed to maximize the coherence between the predicted phenotype-gene set relations and the known disease phenotype-gene associations. An efficient algorithm coupling ridge regression with label propagation and two variants are designed to find the optimal solution to the objective functions of the learning framework.
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Proteases in malaria parasites - a phylogenomic perspective.
Curr. Genomics
PUBLISHED: 05-20-2011
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Malaria continues to be one of the most devastating global health problems due to the high morbidity and mortality it causes in endemic regions. The search for new antimalarial targets is of high priority because of the increasing prevalence of drug resistance in malaria parasites. Malarial proteases constitute a class of promising therapeutic targets as they play important roles in the parasite life cycle and it is possible to design and screen for specific protease inhibitors. In this mini-review, we provide a phylogenomic overview of malarial proteases. An evolutionary perspective on the origin and divergence of these proteases will provide insights into the adaptive mechanisms of parasite growth, development, infection, and pathogenesis.B.
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Principles of hatchet-skin flap for repair of tissue defects on the cheek.
Aesthetic Plast Surg
PUBLISHED: 01-12-2011
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To date, cheek reconstruction remains a challenge for plastic surgeons. This report presents the authors cheek reconstruction technique with different types of hatchet-skin flaps, which provides satisfactory results.
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[Application principles of hatchet skin flap for repairing tissue defect of cheek].
Zhonghua Wai Ke Za Zhi
PUBLISHED: 11-09-2010
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To evaluate the design principles, clinical results and significance of hatchet skin flaps for repairing tissue defects in different parts of cheek.
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[Effect of the tumescent infiltration solution temperature on body temperature].
Zhonghua Zheng Xing Wai Ke Za Zhi
PUBLISHED: 11-05-2010
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To evaluate the effect of tumescent infiltration solution temperature on core body temperature after liposuction.
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Integrative classification and analysis of multiple arrayCGH datasets with probe alignment.
Bioinformatics
PUBLISHED: 07-21-2010
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Array comparative genomic hybridization (arrayCGH) is widely used to measure DNA copy numbers in cancer research. ArrayCGH data report log-ratio intensities of thousands of probes sampled along the chromosomes. Typically, the choices of the locations and the lengths of the probes vary in different experiments. This discrepancy in choosing probes poses a challenge in integrated classification or analysis across multiple arrayCGH datasets. We propose an alignment-based framework to integrate arrayCGH samples generated from different probe sets. The alignment framework seeks an optimal alignment between the probe series of one arrayCGH sample and the probe series of another sample, intended to find the maximum possible overlap of DNA copy number variations between the two measured chromosomes. An alignment kernel is introduced for integrative patient sample classification and a multiple alignment algorithm is also introduced for identifying common regions with copy number aberrations.
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[Application of hatchet flap for buccal tissue defect].
Zhonghua Zheng Xing Wai Ke Za Zhi
PUBLISHED: 12-25-2009
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To evaluate the therapeutic effect of hatchet flap for buccal tissue defect.
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A hypergraph-based learning algorithm for classifying gene expression and arrayCGH data with prior knowledge.
Bioinformatics
PUBLISHED: 07-30-2009
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Incorporating biological prior knowledge into predictive models is a challenging data integration problem in analyzing high-dimensional genomic data. We introduce a hypergraph-based semi-supervised learning algorithm called HyperPrior to classify gene expression and array-based comparative genomic hybridization (arrayCGH) data using biological knowledge as constraints on graph-based learning. HyperPrior is a robust two-step iterative method that alternatively finds the optimal labeling of the samples and the optimal weighting of the features, guided by constraints encoding prior knowledge. The prior knowledge for analyzing gene expression data is that cancer-related genes tend to interact with each other in a protein-protein interaction network. Similarly, the prior knowledge for analyzing arrayCGH data is that probes that are spatially nearby in their layout along the chromosomes tend to be involved in the same amplification or deletion event. Based on the prior knowledge, HyperPrior imposes a consistent weighting of the correlated genomic features in graph-based learning.
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Improved prediction of malaria degradomes by supervised learning with SVM and profile kernel.
Genetica
PUBLISHED: 05-09-2009
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The spread of drug resistance through malaria parasite populations calls for the development of new therapeutic strategies. However, the seemingly promising genomics-driven target identification paradigm is hampered by the weak annotation coverage. To identify potentially important yet uncharacterized proteins, we apply support vector machines using profile kernels, a supervised discriminative machine learning technique for remote homology detection, as a complement to the traditional alignment based algorithms. In this study, we focus on the prediction of proteases, which have long been considered attractive drug targets because of their indispensable roles in parasite development and infection. Our analysis demonstrates that an abundant and complex repertoire is conserved in five Plasmodium parasite species. Several putative proteases may be important components in networks that mediate cellular processes, including hemoglobin digestion, invasion, trafficking, cell cycle fate, and signal transduction. This catalog of proteases provides a short list of targets for functional characterization and rational inhibitor design.
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Module-based subnetwork alignments reveal novel transcriptional regulators in malaria parasite Plasmodium falciparum.
BMC Syst Biol
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Malaria causes over one million deaths annually, posing an enormous health and economic burden in endemic regions. The completion of genome sequencing of the causative agents, a group of parasites in the genus Plasmodium, revealed potential drug and vaccine candidates. However, genomics-driven target discovery has been significantly hampered by our limited knowledge of the cellular networks associated with parasite development and pathogenesis. In this paper, we propose an approach based on aligning neighborhood PPI subnetworks across species to identify network components in the malaria parasite P. falciparum.
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Quantitative proteomic analysis reveals that antioxidation mechanisms contribute to cold tolerance in plantain (Musa paradisiaca L.; ABB Group) seedlings.
Mol. Cell Proteomics
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Banana and its close relative, plantain are globally important crops and there is considerable interest in optimizing their cultivation. Plantain has superior cold tolerance compared with banana and a thorough understanding of the molecular mechanisms and responses of plantain to cold stress has great potential value for developing cold tolerant banana cultivars. In this study, we used iTRAQ-based comparative proteomic analysis to investigate the temporal responses of plantain to cold stress. Plantain seedlings were exposed for 0, 6, and 24 h of cold stress at 8 °C and subsequently allowed to recover for 24 h at 28 °C. A total of 3477 plantain proteins were identified, of which 809 showed differential expression from the three treatments. The majority of differentially expressed proteins were predicted to be involved in oxidation-reduction, including oxylipin biosynthesis, whereas others were associated with photosynthesis, photorespiration, and several primary metabolic processes, such as carbohydrate metabolic process and fatty acid beta-oxidation. Western blot analysis and enzyme activity assays were performed on seven differentially expressed, cold-response candidate plantain proteins to validate the proteomics data. Similar analyses of the seven candidate proteins were performed in cold-sensitive banana to examine possible functional conservation, and to compare the results to equivalent responses between the two species. Consistent results were achieved by Western blot and enzyme activity assays, demonstrating that the quantitative proteomics data collected in this study are reliable. Our results suggest that an increase of antioxidant capacity through adapted ROS scavenging capability, reduced production of ROS, and decreased lipid peroxidation contribute to molecular mechanisms for the increased cold tolerance in plantain. To the best of our knowledge, this is the first report of a global investigation on molecular responses of plantain to cold stress by proteomic analysis.
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Transcriptome profiling of resistant and susceptible Cavendish banana roots following inoculation with Fusarium oxysporum f. sp. cubense tropical race 4.
BMC Genomics
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Fusarium wilt, caused by the fungal pathogen Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4), is considered the most lethal disease of Cavendish bananas in the world. The disease can be managed in the field by planting resistant Cavendish plants generated by somaclonal variation. However, little information is available on the genetic basis of plant resistance to Foc TR4. To a better understand the defense response of resistant banana plants to the Fusarium wilt pathogen, the transcriptome profiles in roots of resistant and susceptible Cavendish banana challenged with Foc TR4 were compared.
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Co-clustering phenome-genome for phenotype classification and disease gene discovery.
Nucleic Acids Res.
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Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype-gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype-gene association matrix under the prior knowledge from phenotype similarity network and protein-protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype-gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein-protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.