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Genes encoded by KEGG pathway (1)

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Takes a KEGG pathway (e.g. hsa00232), finds the proteins that those genes code for and returns the sequences of those proteins.

Created: 2010-04-30 | Last updated: 2010-05-01

Get KEGG gene descriptions and pathways (1)

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This workflow takes a list of KEGG gene identifiers and supplies descriptions associated to said genes + pathways including all genes and the descriptions associated to said pathways. The list_to_string local beanshell scripts merely transform a given list into a string of unique not-null elements separated by new lines (for batch btit use). Note that the input is a real taverna list : multiple values must be declared as multiple values instead of a single string value with distinct identif...

Created: 2010-04-30 | Last updated: 2010-04-30

Credits: Nadia Cerezo Paul Fisher

Attributions: Get Kegg Gene information

Transaction Analysis Demo from RM 5 Intro Day (1)

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This is the demo process presented at the RapidMiner 5 Intro Day. It combines customer segmentation with direct mailing. It loads some transaction data, aggregates and pivotes the data so it can be used by a clustering to perform a customer segmentation. Then, additional data is joined with the clustered data. First, response/no-response data is joined, and them some additional information about the users is added. Finally, customers are classified into response/no-response classes. The dat...

Created: 2010-04-30 | Last updated: 2010-05-05

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Presentation - myExperiment: where work flows

Created: 2010-04-30 07:52:34 | Last updated: 2010-04-30 07:52:49

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Stacking (1)

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RapidMiner supports Meta Learning by embedding one or several basic learners as children into a parent meta learning operator. Here, we use a three base learners inside the stacking operator: decision tree induction, linear regression, and a nearest neighbours classifier. Finally, a Naive Bayes learner is used as a stacking learner which uses the predictions of the preceeding three learners to make a combined prediction.

Created: 2010-04-29

Crossvalidation with SVM (1)

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Performs a crossvalidation on a given data set with nominal label, using a Support Vector Machine as a learning algorithm. Inside the cross validation, the first subprocess generates an SVM model, and the second subprocess evaluates it. applying it on a so-far unused subset of the data and counting the misclassifications.

Created: 2010-04-29

Looping over Examples for doing de-aggrega... (1)

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This process is based on (artificially generated) data that looks like it has been aggregated before. The integer attribute Qty specifies the quantity of the given item that is represented by the rest of the example. The process now loops over every example and performs on each example another loop, that will append the current example to a new example set. This example set has been created as empty copy of the original example set, so that the attributes are equally. To get access to and rem...

Created: 2010-04-29

ChEBI mashup from searched string (1)

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A demo showing hoe to use SOAP services from Bio2RDF ChEBI SPARQL endpoint.A demo showing hoe to use SOAP services from Bio2RDF ChEBI SPARQL endpoint. First a full text search query is done, then we do a reverse link query to get all the related topic. Finally with a describe queries we obtain the complete graph of each topic. The ntriples result is a mashup of what is known about this searched topic form ChEBI. This graph can then be loaded in a triplestore for further exploration.

Created: 2010-04-29

Credits: Francois Belleau

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Presentation - myExperiment Supporting social curati...

Created: 2010-04-27 14:20:54 | Last updated: 2010-04-27 14:21:19

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Presentation - Research Objects: towards exchange an...

Created: 2010-04-26 20:13:05 | Last updated: 2010-04-26 20:21:10

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