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  • 1
    Language: English
    In: PLoS ONE, 2011, Vol.6(10), p.e25364
    Description: Diagnostic and prognostic biomarkers for cancer based on gene expression profiles are viewed as a major step towards a better personalized medicine. Many studies using various computational approaches have been published in this direction during the last decade. However, when comparing different gene signatures for related clinical questions often only a small overlap is observed. This can have various reasons, such as technical differences of platforms, differences in biological samples or their treatment in lab, or statistical reasons because of the high dimensionality of the data combined with small sample size, leading to unstable selection of genes. In conclusion retrieved gene signatures are often hard to interpret from a biological point of view. We here demonstrate that it is possible to construct a consensus signature from a set of seemingly different gene signatures by mapping them on a protein interaction network. Common upstream proteins of close gene products, which we identified via our developed algorithm, show a very clear and significant functional interpretation in terms of overrepresented KEGG pathways, disease associated genes and known drug targets. Moreover, we show that such a consensus signature can serve as prior knowledge for predictive biomarker discovery in breast cancer. Evaluation on different datasets shows that signatures derived from the consensus signature reveal a much higher stability than signatures learned from all probesets on a microarray, while at the same time being at least as predictive. Furthermore, they are clearly interpretable in terms of enriched pathways, disease associated genes and known drug targets. In summary we thus believe that network based consensus signatures are not only a way to relate seemingly different gene signatures to each other in a functional manner, but also to establish prior knowledge for highly stable and interpretable predictive biomarkers.
    Keywords: Research Article ; Biology ; Computer Science ; Medicine ; Genetics And Genomics ; Molecular Biology ; Computational Biology ; Oncology ; Computer Science ; Pathology ; Biochemistry
    E-ISSN: 1932-6203
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  • 2
    In: PLoS ONE, 2013, Vol.8(9)
    Description: Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typical low reproducibility of signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. We here propose a technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier. This is done by smoothing t-statistics of individual genes or miRNAs over the structure of a combined protein-protein interaction (PPI) and miRNA-target gene network. A permutation test is conducted to select features in a highly consistent manner, and subsequently a Support Vector Machine (SVM) classifier is trained. Compared to several other competing methods our algorithm reveals an overall better prediction performance for early versus late disease relapse and a higher signature stability. Moreover, obtained gene lists can be clearly associated to biological knowledge, such as known disease genes and KEGG pathways. We demonstrate that our data integration strategy can improve classification performance compared to using a single data source only. Our method, called stSVM, is available in R-package netClass on CRAN ( http://cran.r-project.org ).
    Keywords: Research Article ; Biology ; Computer Science ; Engineering ; Mathematics ; Medicine
    E-ISSN: 1932-6203
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  • 3
    In: PLoS ONE, 2014, Vol.9(3)
    Description: The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are given weight according to the feature’s relevance and dissimilarity among the features, respectively. The problem of finding relevant and non-redundant features is then mapped into densest subgraph finding problem. We have proposed a multiobjective particle swarm optimization (PSO)-based algorithm that optimizes average node-weight and average edge-weight of the candidate subgraph simultaneously. The proposed algorithm is applied for identifying relevant and non-redundant disease-related genes from microarray gene expression data. The performance of the proposed method is compared with that of several other existing feature selection techniques on different real-life microarray gene expression datasets.
    Keywords: Research Article ; Biology ; Computer Science ; Engineering ; Mathematics ; Medicine
    E-ISSN: 1932-6203
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  • 4
    In: PLoS ONE, 2015, Vol.10(6)
    Description: Background The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics. Methods We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α , β)-k -Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone. Results Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 5
    In: PLoS ONE, 2013, Vol.8(11)
    Description: Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci. Multiple testing methods in microarray data analysis aim at controlling both Type I and Type II error rates; however, real microarray data do not always fit their distribution assumptions. Smyth's ubiquitous parametric method, for example, inadequately accommodates violations of normality assumptions, resulting in inflated Type I error rates. The Significance Analysis of Microarrays, another widely used microarray data analysis method, is based on a permutation test and is robust to non-normally distributed data; however, the Significance Analysis of Microarrays method fold change criteria are problematic, and can critically alter the conclusion of a study, as a result of compositional changes of the control data set in the analysis. We propose a novel approach, combining resampling with empirical Bayes methods: the Resampling-based empirical Bayes Methods. This approach not only reduces false discovery rates for non-normally distributed microarray data, but it is also impervious to fold change threshold since no control data set selection is needed. Through simulation studies, sensitivities, specificities, total rejections, and false discovery rates are compared across the Smyth's parametric method, the Significance Analysis of Microarrays, and the Resampling-based empirical Bayes Methods. Differences in false discovery rates controls between each approach are illustrated through a preterm delivery methylation study. The results show that the Resampling-based empirical Bayes Methods offer significantly higher specificity and lower false discovery rates compared to Smyth's parametric method when data are not normally distributed. The Resampling-based empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes is large for both normally and non-normally distributed data. Finally, the Resampling-based empirical Bayes Methods are generalizable to next generation sequencing RNA-seq data analysis.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 6
    In: Computational and Mathematical Methods in Medicine, 2012, Vol.2012, 2 pages
    Description: Revolutions in biotechnology and information technology have produced enormous amounts of biomedical data. Processing and analysis of these data are accelerating the expansion of our knowledge of biological systems. These advances are changing the way biomedical research, development, and applications are done. Clinical data complement the basic biology data, enabling detailed descriptions and modeling of various healthy and diseased states, disease progression, and the responses to therapies. The availability of data representing various biological states, processes, and their time dependencies enables the study of biological systems at various levels of organization, from molecule to whole organism, and even at population levels. Multiple sources of data support a rapidly growing body of biomedical knowledge; however, our ability to analyze and interpret these data lags far behind data generation and storage capacity. Computational models are increasingly used to help interpret biomedical data produced by high-throughput genomics, proteomics, and immunomics projects [1–3]. Advanced applications of computer models that enable the simulation of biological processes are used to generate hypotheses and plan experiments [4–7]. Appropriately interfaced with biomedical databases, computational models enable rapid access to higher-level knowledge and its sharing through data mining and knowledge discovery approaches.
    Keywords: Medicine;
    ISSN: 1748-670X
    E-ISSN: 1748-6718
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  • 7
    Language: English
    In: Chemie Ingenieur Technik, June 2012, Vol.84(6), pp.905-917
    Description: Membrane technology plays more and more a crucial role in the purification of biotechnological products. Integration of membrane based unit operations becomes a trend for ongoing process designs. By this, in addition to the well‐established membrane unit operations like microfiltration, ultrafiltration, nanofiltration and reverse osmosis, new membranes, modules and systems were developed in the last years. Herein, the efforts in the area of membrane chromatography should be mentioned as a major research topic. This paper focuses on the state of the art in membrane technology, especially in the field of biotechnology, and on innovative developments in the field of membrane chromatography as well as on process design methods, which are necessary to fulfill the challenges for competitive technologies for the future. To minimize the risk that is inherent in the design of any new process, it is essential to use unit operation models that describe the process behavior accurately. Modeling efforts, which were originally developed for other membrane unit operations, show a great potential for the adaption to new developed membrane technologies. An important challenge in the integration of membrane unit operations into bioprocesses is the scale‐up into industrial scale. Based on the state of the art, today's membrane systems, scaling techniques and research efforts are described and future trends are shown.
    Keywords: Biotechnology ; Membrane ; Process Design ; Process Development ; Separation Technology
    ISSN: 0009-286X
    E-ISSN: 1522-2640
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  • 8
    In: PLoS ONE, 2014, Vol.9(10)
    Description: We previously carried out the design and testing of a custom-built Haemophilus influenzae supragenome hybridization (SGH) array that contains probe sequences to 2,890 gene clusters identified by whole genome sequencing of 24 strains of H. influenzae . The array was originally designed as a tool to interrogate the gene content of large numbers of clinical isolates without the need for sequencing, however, the data obtained is quantitative and is thus suitable for transcriptomic analyses. In the current study RNA was extracted from H. influenzae strain CZ4126/02 (which was not included in the design of the array) converted to cDNA, and labelled and hybridized to the SGH arrays to assess the quality and reproducibility of data obtained from these custom-designed chips to serve as a tool for transcriptomics. Three types of experimental replicates were analyzed with all showing very high degrees of correlation, thus validating both the array and the methods used for RNA profiling. A custom filtering pipeline for two-condition unpaired data using five metrics was developed to minimize variability within replicates and to maximize the identification of the most significant true transcriptional differences between two samples. These methods can be extended to transcriptional analysis of other bacterial species utilizing supragenome-based arrays.
    Keywords: Research Article ; Biology And Life Sciences ; Physical Sciences ; Computer And Information Sciences ; Research And Analysis Methods ; Medicine And Health Sciences
    E-ISSN: 1932-6203
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  • 9
    Language: English
    In: Blood, 26 January 2017, Vol.129(4), pp.460-472
    Description: Epithelial-to-mesenchymal-transition (EMT) is critical for normal embryogenesis and effective postnatal wound healing, but is also associated with cancer metastasis. SNAIL, ZEB, and TWIST families of transcription factors are key modulators of the EMT process, but their precise roles in adult hematopoietic development and homeostasis remain unclear. Here we report that genetic inactivation of Zeb2 results in increased frequency of stem and progenitor subpopulations within the bone marrow (BM) and spleen and that these changes accompany differentiation defects in multiple hematopoietic cell lineages. We found no evidence that Zeb2 is critical for hematopoietic stem cell self-renewal capacity. However, knocking out Zeb2 in the BM promoted a phenotype with several features that resemble human myeloproliferative disorders, such as BM fibrosis, splenomegaly, and extramedullary hematopoiesis. Global gene expression and intracellular signal transduction analysis revealed perturbations in specific cytokine and cytokine receptor-related signaling pathways following Zeb2 loss, especially the JAK-STAT and extracellular signal-regulated kinase pathways. Moreover, we detected some previously unknown mutations within the human Zeb2 gene (ZFX1B locus) from patients with myeloid disease. Collectively, our results demonstrate that Zeb2 controls adult hematopoietic differentiation and lineage fidelity through widespread modulation of dominant signaling pathways that may contribute to blood disorders.
    Keywords: Cytokines -- Genetics ; Epithelial-Mesenchymal Transition -- Genetics ; Hematopoiesis, Extramedullary -- Genetics ; Homeodomain Proteins -- Genetics ; Primary Myelofibrosis -- Genetics ; Repressor Proteins -- Genetics ; Splenomegaly -- Genetics
    ISSN: 00064971
    E-ISSN: 1528-0020
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  • 10
    Language: English
    In: Immunogenetics, 2014, Vol.66(5), pp.287-297
    Description: Recently, evidence was provided for common familial occurrence of systemic mast cell activation disease (MCAD), i.e., mast cell disorders characterized by aberrant release of mast cell mediators and/or accumulation of pathological mast cells in potentially any tissue. Since there is accumulating evidence that epigenetic processes may have transgenerational consequences, the aim of the present study was to investigate by two different experimental approaches whether epigenetic effects may contribute to the familial occurrence of MCAD. (1) High throughput profiling of the methylation status of the genomic DNA in leukocytes from MCAD patients in comparison to healthy subjects revealed for the first time an association of MCAD with alterations in DNA methylation comprising genes encoding proteins crucially involved in DNA/RNA repair and processing, apoptosis, cell activity, and exocytosis/cell communication. A set of 195 differentially methylated CpG sites could be regarded as candidates for a MCAD signature at the methylation level of the DNA. (2) In a cohort of MCAD patients, a correlation between age at symptom onset and year of birth (reflecting different generations) was observed suggesting the presence of the phenomenon of anticipation. In conclusion, the present findings suggest that epigenetic processes could substantially contribute to the transgenerational transmission of MCAD.
    Keywords: Systemic mast cell activation disease ; Systemic mastocytosis ; Systemic mast cell activation syndrome ; Methylation ; Anticipation ; Epigenetics
    ISSN: 0093-7711
    E-ISSN: 1432-1211
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