Parallel classification and feature selection in microarray data using SPRINT.

Lawrence Mitchell, Terence M Sloan, Muriel Mewissen, Peter Ghazal, Thorsten Forster, Michal Piotrowski, Arthur Trew

Abstract read

In one paragraph

Article in Concurrency and computation : practice & experience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing 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.

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Lawrence MitchellEPCC, School of Physics and Astronomy, University of Edinburgh, Edinburgh, EH9 3JZ, UK.

Terence M SloanEPCC, School of Physics and Astronomy, University of Edinburgh, Edinburgh, EH9 3JZ, UK.

Muriel MewissenDivision of Pathway Medicine, University of Edinburgh, Medical School, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.

Peter GhazalDivision of Pathway Medicine, University of Edinburgh, Medical School, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.

Thorsten ForsterDivision of Pathway Medicine, University of Edinburgh, Medical School, 49 Little France Crescent, Edinburgh, EH16 4SB, UK.

Michal PiotrowskiEPCC, School of Physics and Astronomy, University of Edinburgh, Edinburgh, EH9 3JZ, UK.

Arthur TrewEPCC, School of Physics and Astronomy, University of Edinburgh, Edinburgh, EH9 3JZ, UK.

Funding

Biotechnology and Biological Sciences Research Council BB/J019283/1Wellcome Trust 086696

6 · The paper itself

Abstract

The statistical language R is favoured by many biostatisticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming or even not possible at all with the existing software infrastructure. High performance computing (HPC) systems offer a solution to these problems but at the expense of increased complexity for the end user. The Simple Parallel R Interface is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop-in parallelised replacements of existing R functions. In this paper we describe parallel implementations of two popular techniques: exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method.

Indexed as

GenomicsHPCParallel programming

Identifiers

PMID24883047

DOI10.1002/cpe.2928

PMCPMC4038771

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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.