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  • 1
    Article
    Article
    In: International Journal of Cancer, 01 January 2016, Vol.138(1), pp.9-9
    Description: Byline: Peter Lichter ***** No abstract is available for this article. *****
    Keywords: Medicine;
    ISSN: 0020-7136
    E-ISSN: 1097-0215
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  • 2
    In: International Journal of Cancer, 01 January 2017, Vol.140(1), pp.9-9
    Description: Byline: Peter Lichter ***** No abstract is available for this article. *****
    Keywords: Cancer Research;
    ISSN: 0020-7136
    E-ISSN: 1097-0215
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  • 3
    Language: English
    In: International journal of cancer, 01 September 2017, Vol.141(5), pp.866
    Description: Byline: Peter Lichter, Christof von Kalle ***** No abstract is available for this article. *****
    Keywords: Translational Medical Research ; Neoplasms -- Genetics
    ISSN: 00207136
    E-ISSN: 1097-0215
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  • 4
    Language: English
    In: BMC Bioinformatics, May 9, 2011, Vol.12, p.138
    Description: Background Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net. We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone. Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Results Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L.sub.1 ) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error. Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. Conclusions The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters. The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'. We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets.
    Keywords: Genetic Algorithms -- Usage ; Genetic Algorithms -- Research
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 5
    Language: English
    In: BMC Bioinformatics, May 9, 2011, Vol.12, p.138
    Description: Background Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net. We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone. Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Results Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (L.sub.1 ) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error. Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations. Conclusions The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters. The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'. We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets.
    Keywords: Genetic Algorithms -- Usage ; Genetic Algorithms -- Research
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 6
    In: Nature Reviews Genetics, 2013, Vol.14(11), p.765
    Description: Malignancies are characterized by extensive global reprogramming of epigenetic patterns, including gains or losses in DNA methylation and changes to histone marks. Furthermore, high-resolution genome-sequencing efforts have discovered a wealth of mutations in genes encoding epigenetic regulators that have roles as 'writers', 'readers' or 'editors' of DNA methylation and/or chromatin states. In this Review, we discuss how these mutations have the potential to deregulate hundreds of targeted genes genome wide. Elucidating these networks of epigenetic factors will provide mechanistic understanding of the interplay between genetic and epigenetic alterations, and will inform novel therapeutic strategies.
    Keywords: Genomes ; Malignancy ; Histones ; Chromatin ; Epigenetics ; DNA Methylation ; Mutation ; Cancer ; Human Genetics ; DNA Metabolism & Structure;
    ISSN: 1471-0056
    E-ISSN: 14710064
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  • 7
    Language: English
    In: PLoS ONE, 2011, Vol.6(7), p.e22146
    Description: MicroRNAs are 22 nucleotides long non-coding RNAs and exert their function either by transcriptional or translational inhibition. Although many microRNA profiles in different tissues and disease states have already been discovered, only little is known about their target proteins. The microRNA miR-155 is deregulated in many diseases, including cancer, where it might function as an oncoMir. ; We employed a proteomics technique called “stable isotope labelling by amino acids in cell culture” (SILAC) allowing relative quantification to reliably identify target proteins of miR-155. Using SILAC, we identified 46 putative miR-155 target proteins, some of which were previously reported. With luciferase reporter assays, CKAP5 was confirmed as a new target of miR-155. Functional annotation of miR-155 target proteins pointed to a role in cell cycle regulation. ; To the best of our knowledge we have investigated for the first time miR-155 target proteins in the HEK293T cell line in large scale. In addition, by comparing our results to previously identified miR-155 target proteins in other cell lines, we provided further evidence for the cell line specificity of microRNAs.
    Keywords: Research Article ; Biology ; Biochemistry
    E-ISSN: 1932-6203
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  • 8
    Language: English
    In: Cancer Cell, 2010, Vol.17(1), pp.3-4
    Description: miR-15a and miR-16-1 were the first microRNAs linked to cancer because their genes are commonly deleted in human chronic lymphocytic leukemia (CLL). In this issue of , Klein and coworkers show that deleting a region with these genes in mouse provides a faithful model for human CLL.
    Keywords: Medicine
    ISSN: 1535-6108
    E-ISSN: 1878-3686
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  • 9
    Language: English
    In: International Journal of Cancer, 01 July 2008, Vol.123(1), pp.ix-ix
    Description: Abstract not available.
    Keywords: Epigenetics ; Carcinogenesis ; Gene Regulation ; Other;
    ISSN: 0020-7136
    E-ISSN: 1097-0215
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  • 10
    Language: English
    In: Cancer Research, 10/01/2014, Vol.74(19 Supplement), pp.LB-203-LB-203
    ISSN: 0008-5472
    E-ISSN: 1538-7445
    Source: CrossRef
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