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  • 1
    Language: English
    In: BMC Bioinformatics, Nov 12, 2010, Vol.11, p.556
    Description: Background Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm. Results We compare the performance of several normalisation methods that have been established for dual-colour gene expression microarrays. The focus is on an invariant selection algorithm, for which effective improvements are proposed. In a simulation study the performances of the different normalisation methods are compared with respect to their impact on the ability to correctly detect differentially expressed features. Furthermore, we apply the different normalisation methods to a pancreatic cancer data set to assess the impact on the classification power. Conclusions The simulation study and the data application demonstrate the superior performance of the improved invariant selection algorithms in comparison to other normalisation methods, especially in situations where the assumptions of the usual global loess normalisation are violated.
    Keywords: Gene Expression -- Research ; Dna Microarrays -- Methods ; Dna Microarrays -- Research ; Optimization Theory -- Research ; Antibodies -- Research
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 2
    In: Nature, 2018
    Description: Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.
    Keywords: DNA Methylation ; Tumors ; Standardization ; Data Processing ; Classification ; Methylation ; Brain Cancer ; Bioinformatics ; Cancer ; Generalized Linear Models ; DNA Methylation ; Diagnosis ; Tumors ; Genomes ; Classification ; Central Nervous System ; Central Nervous System ; Diagnosis ; Cancer ; Learning Algorithms ; Diagnostic Software ; Data Processing ; Tumors ; Central Nervous System ; Gene Expression ; Standardization ; Classification ; Cancer ; Classifiers ; Classification ; Clinical Trials ; Deoxyribonucleic Acid–DNA ; Probability ; Diagnostic Systems ; Nervous System ; Methylation ; Data Processing ; Tumors ; Data Processing ; Deoxyribonucleic Acid–DNA ; Deoxyribonucleic Acid–DNA ; World Health Organization;
    ISSN: 0028-0836
    E-ISSN: 1476-4687
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  • 3
    Language: English
    In: Cancer Research, 04/15/2010, Vol.70(8 Supplement), pp.1739-1739
    ISSN: 0008-5472
    E-ISSN: 1538-7445
    Source: CrossRef
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  • 4
    Language: English
    In: Solar Energy Materials and Solar Cells, Jan, 2012, Vol.96(1), p.226(5)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.solmat.2011.09.062 Byline: Guobin Jia, Martin Steglich, Ingo Sill, Fritz Falk Keywords: Silicon; Nanowires; Heterojunction; a-Si; Solar cell; Metal assisted wet chemical etching Abstract: Efficient core-shell TCO/a-Si/Si nanowires (SiNWs) heterojunction solar cells were fabricated on SiNW arrays prepared by metal assisted wet chemical etching of an n-type silicon wafer. The silver catalyst was carefully removed after the etching by a three-step procedure. A stack of intrinsic and p-type amorphous-Si (a-Si) was deposited as a shell onto the SiNW arrays by plasma enhanced chemical vapor deposition (PECVD), and finally, a [approximately equal to]200nm TCO layer was deposited on top of the a-Si layer by atomic layer deposition (ALD). No shunt was detected in our cells, which was a big problem in the cells prepared on similar substrates published in the literature. The core-shell heterojunction solar cells on nanowire arrays also show great improvement of the performance in comparison with those published previously. In a mesa-structured solar cell with contact area of 7mm.sup.2, an open circuit voltage of 476mV, short circuit current density of 27mA/cm.sup.2, filling factor of 56.2% and conversion efficiency of 7.29% was determined at AM 1.5. Electron beam induced current (EBIC) measurements were performed on the solar cells, which demonstrate unambiguously that the nanowire arrays work as active photovoltaic components. Author Affiliation: Institute of Photonic Technology, Albert-Einstein-Str. 9, 07745 Jena, Germany Article History: Received 24 May 2011; Revised 27 September 2011; Accepted 28 September 2011
    Keywords: Solar Energy Industry -- Analysis ; Silicon -- Analysis ; Nanotechnology -- Analysis ; Chemical Vapor Deposition -- Analysis ; Solar Cells -- Analysis
    ISSN: 0927-0248
    E-ISSN: 18793398
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  • 5
    Language: English
    In: BMC Bioinformatics, Nov 12, 2010, Vol.11, p.556
    Description: Background Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm. Results We compare the performance of several normalisation methods that have been established for dual-colour gene expression microarrays. The focus is on an invariant selection algorithm, for which effective improvements are proposed. In a simulation study the performances of the different normalisation methods are compared with respect to their impact on the ability to correctly detect differentially expressed features. Furthermore, we apply the different normalisation methods to a pancreatic cancer data set to assess the impact on the classification power. Conclusions The simulation study and the data application demonstrate the superior performance of the improved invariant selection algorithms in comparison to other normalisation methods, especially in situations where the assumptions of the usual global loess normalisation are violated.
    Keywords: Gene Expression -- Research ; Dna Microarrays -- Methods ; Dna Microarrays -- Research ; Optimization Theory -- Research ; Antibodies -- Research
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 6
    Language: English
    In: Cell, 25 February 2016, Vol.164(5), pp.1060-1072
    Description: Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. From the remaining fraction of CNS-PNETs, we identify four new CNS tumor entities, each associated with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated “CNS neuroblastoma with activation (CNS NB- ),” “CNS Ewing sarcoma family tumor with alteration (CNS EFT- ),” “CNS high-grade neuroepithelial tumor with alteration (CNS HGNET- ),” and “CNS high-grade neuroepithelial tumor with alteration (CNS HGNET- ),” will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by poorly differentiated CNS tumors. Highly malignant primitive neuroectodermal tumors of the CNS (CNS-PNETs) have been challenging to diagnose and distinguish from other kinds of brain tumors, but molecular profiling now reveals that these cancers can be readily classified into some known tumor types and four new entities with distinct histopathological and clinical features, paving the way for meaningful clinical trials.
    Keywords: Biology
    ISSN: 0092-8674
    E-ISSN: 1097-4172
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  • 7
  • 8
    Language: English
    In: Clinical cancer research : an official journal of the American Association for Cancer Research, 01 December 2016, Vol.22(23), pp.5765-5771
    Description: Antiangiogenic treatment with bevacizumab, a mAb to the VEGF, is the single most widely used therapeutic agent for patients with recurrent glioblastoma. A major challenge is that there are currently no validated biomarkers that can predict treatment outcome. Here we analyze the potential of radiomics, an emerging field of research that aims to utilize the full potential of medical imaging. A total of 4,842 quantitative MRI features were automatically extracted and analyzed from the multiparametric tumor of 172 patients (allocated to a discovery and validation set with a 2:1 ratio) with recurrent glioblastoma prior to bevacizumab treatment. Leveraging a high-throughput approach, radiomic features of patients in the discovery set were subjected to a supervised principal component (superpc) analysis to generate a prediction model for stratifying treatment outcome to antiangiogenic therapy by means of both progression-free and overall survival (PFS and OS). The superpc predictor stratified patients in the discovery set into a low or high risk group for PFS (HR = 1.60; P = 0.017) and OS (HR = 2.14; P 〈 0.001) and was successfully validated for patients in the validation set (HR = 1.85, P = 0.030 for PFS; HR = 2.60, P = 0.001 for OS). Our radiomic-based superpc signature emerges as a putative imaging biomarker for the identification of patients who may derive the most benefit from antiangiogenic therapy, advances the knowledge in the noninvasive characterization of brain tumors, and stresses the role of radiomics as a novel tool for improving decision support in cancer treatment at low cost. Clin Cancer Res; 22(23); 5765-71. ©2016 AACR.
    Keywords: Angiogenesis Inhibitors -- Therapeutic Use ; Brain Neoplasms -- Drug Therapy ; Glioblastoma -- Drug Therapy ; Neoplasm Recurrence, Local -- Drug Therapy
    ISSN: 1078-0432
    E-ISSN: 15573265
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  • 9
    Language: English
    In: The international journal of biostatistics, 2010, Vol.6(1), pp.Article 15
    Description: The analysis of dose-response relationships is a common problem in pre-clinical studies. For example, proportions such as mortality rates and histopathological findings are of particular interest in repeated toxicity studies. Commonly applied designs consist of an untreated control group and several, possibly unequally spaced, dosage groups. The Williams test can be formulated as a multiple contrast test and is a powerful option to evaluate such data. In this paper, we consider simultaneous inference for Williams-type multiple contrasts when the response variable is binomial and sample sizes are only moderate. Approximate simultaneous confidence limits can be constructed using the quantiles of a multivariate normal distribution taking the correlation into account. Alternatively, multiplicity-adjusted p-values can be calculated as well. A simulation study shows that a simple correction based on adding pseudo observations leads to acceptable performance for moderate sample sizes, such as 40 per group. In addition, the calculation of adjusted p-values and approximate power is presented. Finally, the proposed methods are applied to example data from two toxicological studies; the methods are available in an R-package.
    Keywords: Dose-Response Relationship, Drug ; Biometry -- Methods ; Carcinogenicity Tests -- Methods ; Contrast Media -- Toxicity
    E-ISSN: 1557-4679
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  • 10
    Language: English
    In: BMC Medical Genomics, June 3, 2010, Vol.3, p.21
    Description: Background In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. Results We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. Conclusions The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.
    Keywords: Applications Software -- Usage ; Dna Microarrays -- Usage ; Genetic Algorithms -- Usage ; Single Nucleotide Polymorphisms -- Research
    ISSN: 1755-8794
    Source: Cengage Learning, Inc.
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