Literature DB >> 21450711

pathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery.

Marc Johannes1, Holger Fröhlich, Holger Sültmann, Tim Beissbarth.   

Abstract

UNLABELLED: Prognostic and diagnostic biomarker discovery is one of the key issues for a successful stratification of patients according to clinical risk factors. For this purpose, statistical classification methods, such as support vector machines (SVM), are frequently used tools. Different groups have recently shown that the usage of prior biological knowledge significantly improves the classification results in terms of accuracy as well as reproducibility and interpretability of gene lists. Here, we introduce pathClass, a collection of different SVM-based classification methods for improved gene selection and classfication performance. The methods contained in pathClass do not merely rely on gene expression data but also exploit the information that is carried in gene network data. AVAILABILITY: pathClass is open source and freely available as an R-Package on the CRAN repository at http://cran.r-project.org.

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Year:  2011        PMID: 21450711     DOI: 10.1093/bioinformatics/btr157

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  8 in total

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2.  Blood transcriptomic comparison of individuals with and without autism spectrum disorder: A combined-samples mega-analysis.

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Journal:  Am J Med Genet B Neuropsychiatr Genet       Date:  2016-11-11       Impact factor: 3.568

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Journal:  BMC Bioinformatics       Date:  2012-05-01       Impact factor: 3.169

4.  Network and data integration for biomarker signature discovery via network smoothed T-statistics.

Authors:  Yupeng Cun; Holger Fröhlich
Journal:  PLoS One       Date:  2013-09-03       Impact factor: 3.240

5.  The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells.

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6.  A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.

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7.  Integration of gene interaction information into a reweighted random survival forest approach for accurate survival prediction and survival biomarker discovery.

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Journal:  Sci Rep       Date:  2018-09-04       Impact factor: 4.379

Review 8.  Incorporating Machine Learning into Established Bioinformatics Frameworks.

Authors:  Noam Auslander; Ayal B Gussow; Eugene V Koonin
Journal:  Int J Mol Sci       Date:  2021-03-12       Impact factor: 5.923

  8 in total

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