Evidence map›Paper›PMID 26609213›Full record

ArticleCancer informatics2015

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data.

Lie Xiong, Pei-Fen Kuan, Jianan Tian, Sunduz Keles, Sijian Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 88% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Lie XiongDepartment of Statistics, University of Wisconsin, Madison, WI, USA.
Pei-Fen KuanDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
Jianan TianDepartment of Statistics, University of Wisconsin, Madison, WI, USA.
Sunduz KelesDepartment of Statistics, University of Wisconsin, Madison, WI, USA. ; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
Sijian WangDepartment of Statistics, University of Wisconsin, Madison, WI, USA. ; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
University of Wisconsin–Madison · USStony Brook University · US

Funding

Statistical Methods for the Analysis of ChlP-chip DataR01HG003747 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI KELES, SUNDUZ · 2007 to 2024
$4.8M
Statistical Methods For Annotating Repetitive Genomic Regions Through ENCODE-deriU01HG007019 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI BRESNICK, EMERY H, DEWEY, COLIN NOEL · 2012 to 2015
$1.5M
Heterogenous and Robust Survival Analysis in Genomic StudiesR01HG007377 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI WANG, SIJIAN · 2014 to 2017
$1.0M
NHGRI NIH HHS R01 HG003747NHGRI NIH HHS R01 HG007377NHGRI NIH HHS U01 HG007019
6 · The paper itself

Abstract

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies.

Indexed as

boostingbreast cancerintegrative genomic analysismultivariate regression

Identifiers

PMID26609213
PMCPMC4648611
OpenAlexW2174694264

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.