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EC specifications

Information


Unique identifier OMICS_32831
Name EC
Alternative name Evaporative Cooling
Software type Application/Script
Interface Command line interface, Graphical user interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C, C++, Java, Shell (Bash)
License GNU General Public License version 2.0
Computer skills Advanced
Stability Stable
Maintained Yes

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Documentation


Maintainer


  • person_outline Brett McKinney

Publication for Evaporative Cooling

EC citations

 (8)
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The Integration of Epistasis Network and Functional Interactions in a GWAS Implicates RXR Pathway Genes in the Immune Response to Smallpox Vaccine

2016
PLoS One
PMCID: 4981436
PMID: 27513748
DOI: 10.1371/journal.pone.0158016
call_split See protocol

[…] Simulated Evaporative Cooling (EC) is a machine-learning algorithm that incorporates main effect contributions with interaction effects to prioritize variants [,]. The algorithm removes the least important SNPs […]

library_books

More than carbon sequestration: Biophysical climate benefits of restored savanna woodlands

2016
Sci Rep
PMCID: 4931580
PMID: 27373738
DOI: 10.1038/srep29194

[…] climate response and cancelled the impact of increased short-wave radiation due to the decrease in albedo (). On average, the albedo decreased by 1% over all restored regions. The resulting stronger evaporative cooling led to lower surface temperatures, with a cooling of 0.66 °C averaged over all restored areas (). Regionally, the cooling was most pronounced over the restored areas of Queensland […]

library_books

Allele Specific Network Reveals Combinatorial Interaction That Transcends Small Effects in Psoriasis GWAS

2014
PLoS Comput Biol
PMCID: 4168982
PMID: 25233071
DOI: 10.1371/journal.pcbi.1003766

[…] work construction approaches utilizing known information have been introduced to capture combinations of markers with phenotypic associations, such as nested clade analysis , treescanning , simulated evaporative cooling networks , SNPrank , Hua et al.'s SNP-SNP networks , statistical epistasis networks , and Li et al.'s two-step method . These methods utilize phenotypic information and/or biologic […]

library_books

ReliefSeq: A Gene Wise Adaptive K Nearest Neighbor Feature Selection Tool for Finding Gene Gene Interactions and Main Effects in mRNA Seq Gene Expression Data

2013
PLoS One
PMCID: 3858248
PMID: 24339943
DOI: 10.1371/journal.pone.0081527

[…] nks buffering interactions higher than the other methods. This is not surprising given the other analytical tools were not designed to detect buffering interactions. In previous method development of Evaporative Cooling feature selection for GWAS data, we integrated importance scores from Relief-F using the traditional k = 10 nearest neighbors with those from Random Forest as a weighted sum in ord […]

library_books

Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder

2012
PMCID: 3432194
PMID: 22892719
DOI: 10.1038/tp.2012.80

[…] s in the network using an eigenvector centrality algorithm and (4) pathway enrichment based on epistasis network centrality prioritization. We first remove noise SNPs with an optimized version of the evaporative cooling machine learning (ECML) filter. We have shown that the ECML filter, which is based on the combination of Relief-F and Random Forests, has the power to detect both epistatic and mai […]

library_books

Multifactor Dimensionality Reduction as a Filter Based Approach for Genome Wide Association Studies

2011
Front Genet
PMCID: 3268633
PMID: 22303374
DOI: 10.3389/fgene.2011.00080

[…] ate the potential of MDR as a filter-based approach, using MDR modeling to evaluate and rank all univariate effects and two-locus epistatic effects. We compare the use of MDR as a filter approach, to evaporative cooling (EC), another machine learning filtering method (McKinney et al., ), which we use in conjunction with the Genetic Association Interaction Network (GAIN) first proposed by McGill () […]

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EC institution(s)
Department of Genetics, University of Alabama School of Medicine, Birmingham, AL, USA; Departments of Pediatrics, Microbiology and Immunology, Program in Vaccine Sciences, Vanderbilt University Medical Center, Nashville, TE, USA
EC funding source(s)
Supported by NIH Grant No. K25 AI-64625 (PI: BAM).

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