Incorporating auxiliary information for improved prediction using combination of kernel machines.

Xiang Zhan, Debashis Ghosh

Abstract read

In one paragraph

Article in Statistical methodology, 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

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.

Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data.BMC bioinformatics · 2015
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

2 authors.

Xiang ZhanDepartment of Statistics, Pennsylvania State University, University Park, PA 16802, U.S.A.

Debashis GhoshDepartment of Statistics, Pennsylvania State University, University Park, PA 16802, U.S.A. ; Department of Public Health Sciences, Pennsylvania State University, University Park, PA 16802, U.S.A.

Funding

Statistical Methods for Cancer BiomarkersR01CA129102 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GHOSH, DEBASHIS, TAYLOR, JEREMY M.G. · 2009 to 2021

$3.3M

NCI NIH HHS R01 CA129102

6 · The paper itself

Abstract

With evolving genomic technologies, it is possible to get different measures of the same underlying biological phenomenon using different technologies. The goal of this paper is to build a prediction model for an outcome variable Y from covariates X. Besides X, we have surrogate covariates W which are related to X. We want to utilize the information in W to boost the prediction for Y using X. In this paper, we propose a kernel machine-based method to improve prediction of Y by X by incorporating auxiliary information W. By combining single kernel machines, we also propose a hybrid kernel machine predictor, which can yield a smaller prediction error than its constituents. The prediction error of our kernel machine predictors is evaluated using simulations. We also apply our method to a lung cancer dataset and an Alzheimer's disease dataset.

Indexed as

Auxiliary informationCombination of kernelsHybrid predictorKernel ridge regressionMean squared prediction error

Identifiers

PMID25419198

DOI10.1016/j.stamet.2014.08.001

PMCPMC4235751

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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.