Literature DB >> 16940323

Model-based boosting in high dimensions.

Torsten Hothorn1, Peter Bühlmann.   

Abstract

SUMMARY: The R add-on package mboost implements functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least squares, either of parametric linear form or smoothing splines, or regression trees as base learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. AVAILABILITY: Package mboost is available from the Comprehensive R Archive Network (http://CRAN.R-project.org) under the terms of the General Public Licence (GPL).

Mesh:

Year:  2006        PMID: 16940323     DOI: 10.1093/bioinformatics/btl462

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


  13 in total

1.  Observing versus Predicting: Initial Patterns of Filling Predict Long-Term Adherence More Accurately Than High-Dimensional Modeling Techniques.

Authors:  Jessica M Franklin; William H Shrank; Joyce Lii; Alexis K Krumme; Olga S Matlin; Troyen A Brennan; Niteesh K Choudhry
Journal:  Health Serv Res       Date:  2015-04-16       Impact factor: 3.402

2.  An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data.

Authors:  Ming Hao; Yanli Wang; Stephen H Bryant
Journal:  Anal Chim Acta       Date:  2013-11-06       Impact factor: 6.558

Review 3.  Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies.

Authors:  Ji Eun Park; Ho Sung Kim
Journal:  Nucl Med Mol Imaging       Date:  2018-02-01

4.  Testing the additional predictive value of high-dimensional molecular data.

Authors:  Anne-Laure Boulesteix; Torsten Hothorn
Journal:  BMC Bioinformatics       Date:  2010-02-08       Impact factor: 3.169

5.  [The first biologic for rheumatoid arthritis: factors influencing the therapeutic decision].

Authors:  D Pattloch; A Richter; B Manger; R Dockhorn; L Meier; H-P Tony; A Zink; A Strangfeld
Journal:  Z Rheumatol       Date:  2017-04       Impact factor: 1.372

6.  A novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies.

Authors:  Verena Zuber; A Pedro Duarte Silva; Korbinian Strimmer
Journal:  BMC Bioinformatics       Date:  2012-10-31       Impact factor: 3.169

7.  L1 regularization facilitates detection of cell type-specific parameters in dynamical systems.

Authors:  Bernhard Steiert; Jens Timmer; Clemens Kreutz
Journal:  Bioinformatics       Date:  2016-09-01       Impact factor: 6.937

8.  Incidence, determinants, and prognostic value of reverse left ventricular remodelling after primary percutaneous coronary intervention: results of the Acute Myocardial Infarction Contrast Imaging (AMICI) multicenter study.

Authors:  Stefania Funaro; Giuseppe La Torre; Mariapina Madonna; Leonarda Galiuto; Antonio Scarà; Alessandra Labbadia; Emanuele Canali; Antonella Mattatelli; Francesco Fedele; Francesco Alessandrini; Filippo Crea; Luciano Agati
Journal:  Eur Heart J       Date:  2008-12-18       Impact factor: 29.983

9.  SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data.

Authors:  Ramon Diaz-Uriarte
Journal:  BMC Bioinformatics       Date:  2008-01-21       Impact factor: 3.169

10.  Random rotation survival forest for high dimensional censored data.

Authors:  Lifeng Zhou; Hong Wang; Qingsong Xu
Journal:  Springerplus       Date:  2016-08-26
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