685 research outputs found

    Medizinische und gesundheitsökonomische Bewertung der Radiochirurgie zur Behandlung von Hirnmetastasen

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    Background: Expressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets. Findings: Using our new analysis tool, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips® can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi. Conclusions: MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants

    MediPlEx - a tool to combine in silico & experimental gene expression profiles of the model legume Medicago truncatula

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    Henckel K, Küster H, Stutz L, Goesmann A. MediPlEx - a tool to combine in silico and experimental gene expression profiles of the model legume Medicago truncatula. BMC Research Notes. 2010;3(1): 262.BACKGROUND:Expressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets.FINDINGS:Using our new application, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi.CONCLUSIONS:MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants. MediPlEx can freely be used at http://www.cebitec.uni-bielefeld.de/mediplex

    Identification of novel stress-responsive biomarkers from gene expression datasets in tomato roots

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    Published by CSIRO Publishing. This is the Author Accepted Manuscript. This article may be used for personal use only.Abiotic stresses such as heat, drought or salinity have been widely studied individually. Nevertheless, in the nature and in the field, plants and crops are commonly exposed to a different combination of stresses, which often result in a synergistic response mediated by the activation of several molecular pathways that cannot be inferred from the response to each individual stress. By screening microarray data obtained from different plant species and under different stresses, we identified several conserved stress-responsive genes whose expression was differentially regulated in tomato (Solanum lycopersicum L.) roots in response to one or several stresses. We validated 10 of these genes as reliable biomarkers whose expression levels are related to different signalling pathways involved in adaptive stress responses. In addition, the genes identified in this work could be used as general salt-stress biomarkers to rapidly evaluate the response of salt-tolerant cultivars and wild species for which sufficient genetic information is not yet available

    Analysis of microarray and next generation sequencing data for classification and biomarker discovery in relation to complex diseases

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    PhDThis thesis presents an investigation into gene expression profiling, using microarray and next generation sequencing (NGS) datasets, in relation to multi-category diseases such as cancer. It has been established that if the sequence of a gene is mutated, it can result in the unscheduled production of protein, leading to cancer. However, identifying the molecular signature of different cancers amongst thousands of genes is complex. This thesis investigates tools that can aid the study of gene expression to infer useful information towards personalised medicine. For microarray data analysis, this study proposes two new techniques to increase the accuracy of cancer classification. In the first method, a novel optimisation algorithm, COA-GA, was developed by synchronising the Cuckoo Optimisation Algorithm and the Genetic Algorithm for data clustering in a shuffle setup, to choose the most informative genes for classification purposes. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) artificial neural networks are utilised for the classification step. Results suggest this method can significantly increase classification accuracy compared to other methods. An additional method involving a two-stage gene selection process was developed. In this method, a subset of the most informative genes are first selected by the Minimum Redundancy Maximum Relevance (MRMR) method. In the second stage, optimisation algorithms are used in a wrapper setup with SVM to minimise the selected genes whilst maximising the accuracy of classification. A comparative performance assessment suggests that the proposed algorithm significantly outperforms other methods at selecting fewer genes that are highly relevant to the cancer type, while maintaining a high classification accuracy. In the case of NGS, a state-of-the-art pipeline for the analysis of RNA-Seq data is investigated to discover differentially expressed genes and differential exon usages between normal and AIP positive Drosophila datasets, which are produced in house at Queen Mary, University of London. Functional genomic of differentially expressed genes were examined and found to be relevant to the case study under investigation. Finally, after normalising the RNA-Seq data, machine learning approaches similar to those in microarray was successfully implemented for these datasets

    Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

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    Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

    Archetypal solution spaces for clustering gene expression datasets in identification of cancer subtypes

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    Gene expression profiles are essential in identifying different cancer phenotypes. Clustering gene expression datasets can provide accurate identification of cancerous cell lines, but this task is challenging due to the small sample size and high dimensionality. Using the KK-means clustering algorithm we determine the organisation of the solution space for a variety of gene expression datasets using energy landscape theory. The solution space landscapes allow us to understand KK-means performance, and guide more effective use when varying common dataset properties; number of features, number of clusters, and cluster distribution. We find that the landscapes have a single-funnelled structure for the appropriate number of clusters, which is lost when the number of clusters deviates from this. We quantify this landscape structure using a frustration metric and show that it may provide a novel diagnostic tool for the appropriate number of cancer subtypes.Comment: 24 pages, 8 figure

    Mutable composite firefly algorithm for gene selection in microarray based cancer classification

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    Cancer classification is critical due to the strenuous effort required in cancer treatment and the rising cancer mortality rate. Recent trends with high throughput technologies have led to discoveries in terms of biomarkers that successfully contributed to cancerrelated issues. A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. However, the existing hybrid approaches with metaheuristic optimization algorithms in feature selection (specifically in gene selection) are not generalized enough to efficiently classify most cancer microarray data while maintaining a small set of genes. This leads to the classification accuracy and genes subset size problem. Hence, this study proposed to modify the Firefly Algorithm (FA) along with the Correlation-based Feature Selection (CFS) filter for the gene selection task. An improved FA was proposed to overcome FA slow convergence by generating mutable size solutions for the firefly population. In addition, a composite position update strategy was designed for the mutable size solutions. The proposed strategy was to balance FA exploration and exploitation in order to address the local optima problem. The proposed hybrid algorithm known as CFS-Mutable Composite Firefly Algorithm (CFS-MCFA) was evaluated on cancer microarray data for biomarker selection along with the deployment of Support Vector Machine (SVM) as the classifier. Evaluation was performed based on two metrics: classification accuracy and size of feature set. The results showed that the CFS-MCFA-SVM algorithm outperforms benchmark methods in terms of classification accuracy and genes subset size. In particular, 100 percent accuracy was achieved on all four datasets and with only a few biomarkers (between one and four). This result indicates that the proposed algorithm is one of the competitive alternatives in feature selection, which later contributes to the analysis of microarray data

    Transgelin is a TGFβ-inducible gene that regulates osteoblastic and adipogenic differentiation of human skeletal stem cells through actin cytoskeleston organization

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    Regenerative medicine is a novel approach for treating conditions in which enhanced bone regeneration is required. We identified transgelin (TAGLN), a transforming growth factor beta (TGFβ)-inducible gene, as an upregulated gene during in vitro osteoblastic and adipocytic differentiation of human bone marrow-derived stromal (skeletal) stem cells (hMSC). siRNA-mediated gene silencing of TAGLN impaired lineage differentiation into osteoblasts and adipocytes but enhanced cell proliferation. Additional functional studies revealed that TAGLN deficiency impaired hMSC cell motility and in vitro transwell cell migration. On the other hand, TAGLN overexpression reduced hMSC cell proliferation, but enhanced cell migration, osteoblastic and adipocytic differentiation, and in vivo bone formation. In addition, deficiency or overexpression of TAGLN in hMSC was associated with significant changes in cellular and nuclear morphology and cytoplasmic organelle composition as demonstrated by high content imaging and transmission electron microscopy that revealed pronounced alterations in the distribution of the actin filament and changes in cytoskeletal organization. Molecular signature of TAGLN-deficient hMSC showed that several genes and genetic pathways associated with cell differentiation, including regulation of actin cytoskeleton and focal adhesion pathways, were downregulated. Our data demonstrate that TAGLN has a role in generating committed progenitor cells from undifferentiated hMSC by regulating cytoskeleton organization. Targeting TAGLN is a plausible approach to enrich for committed hMSC cells needed for regenerative medicine application
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