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User friendly data mining (7)

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This scientific workflow was designed for the novice data miners to learn how mining algorithms behave when applied on their datasets. The workflow is the responsible for invoking the mining recommender, as well as using the datasets required by the user, thus returning the ranking of algorithms according to the design criteria of the recommender. 

Created: 2013-10-03 | Last updated: 2019-01-08

Credits: respinosa

MrBayesPPtest (13)

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robust connection

Created: 2013-12-11 | Last updated: 2014-11-27

ConsensusTree (15)

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robust interaction

Created: 2013-12-11 | Last updated: 2014-11-27

ConVergenceTreeDiagnosticGeoKS (15)

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robust interaction

Created: 2013-12-11 | Last updated: 2014-11-27

Building a knowledge base for data mining ... (7)

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Scientific workflow for creating the knowledge base. 

Created: 2013-10-03 | Last updated: 2018-09-20

Credits: respinosa

Attributions: Measuring data quality criteria in arff files Data Mining Algorithms

G-language Genome Analysis Environment - R... (2)

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This workflow displays a documentation of G-language GAE programs. If you input the "keywords", this workflow searches for the keyword through the documentations. See http://www.g-language.org/ for more information about the G-language Genome Analysis Environment.

Created: 2010-03-30 | Last updated: 2010-03-30

Credits: cory (Kazuki Oshita)

microRNA to KEGG Pathways and Abstracts (1)

Workflow takes in a text file of microRNAs from microCOSM (at the EBI) and outputs a list of KEGG pathway information, including genes in pathways and pathway abstracts from PubMed. The results can then be used in various text mining applications/workflows to rank the results against a given disease.Workflow takes in a file of microRNAs

Created: 2010-03-17

Credits: Paul Fisher

Attributions: Pathways and Gene annotations for QTL region

DataBiNS with Kegg ID (3)

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Consumes a KEGG gene id and mines for pathway, GO, PubMed and SNP information about that gene

Created: 2009-07-09 | Last updated: 2010-11-22

Credits: Mark Wilkinson Fong Chun Chan

A workflow version of the EMBOSS tutorial (1)

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Designed to show the use of EMBOSS based Soaplab services from Taverna, this workflow has no inputs as all initial values are specified as string constants. A sequence set is fetched using the seqret tool, then simultaneously scanned for predicted transmembrane regions and subjected to a multiple alignment using emma. This alignment is then plotted to a set of PNG images and also used to build a profile using the prophecy and prophet tools.

Created: 2009-07-03 | Last updated: 2009-07-03

Credits: Stian Soiland-Reyes

Attributions: A workflow version of the EMBOSS tutorial