Phenotype Prediction Pipeline statistics

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Phenotype Prediction Pipeline specifications

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Unique identifier OMICS_12703
Name Phenotype Prediction Pipeline
Software type Pipeline/Workflow
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages R
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Charles J. Labuzzetta <>

Publication for Phenotype Prediction Pipeline

Phenotype Prediction Pipeline citation

library_books

Using association rule mining to determine promising secondary phenotyping hypotheses

2014
PMCID: 4059059
PMID: 24932005
DOI: 10.1093/bioinformatics/btu260

[…] biological correctness of the data, we expect that those cases still constitute biologically interesting, though known connections between individual phenotypes., the implementation of the secondary phenotype prediction pipeline relies solely on an association rule mining approach. using the pipeline in conjunction with 7027 unique phenotypes (see section 2.1), the obtained result of 188 new […]


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Phenotype Prediction Pipeline institution(s)
Department of Mathematics, Iowa State University, Ames, IA, USA; Department of Biology, Boston College, Chestnut Hill, MA, USA; Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, NC, USA; Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA; Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Computer Science, College of Charleston, Charleston, SC, USA
Phenotype Prediction Pipeline funding source(s)
This study was supported by the ALS Association and the National Science Foundation DBI Award 1359301 and supported in part by the Hollings Cancer Center, Medical University of South Carolina Support Grant (P30 CA 138313).

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