Workflows

Transcribe DNA (1)

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The Transcribe DNA service takes the DNA sequence, here defaulted to 'gatcctccat' and outputs the corresponding transcribed RNA sequence, in this example, 'gauccuccau'.

Created: 2010-10-28

Reverse complement DNA (1)

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The Reverse Complement DNA service takes the DNA sequence, here defaulted to 'gatcctccat' and outputs the corresponding reverse complement sequence, in this example, 'atggaggatc'.

Created: 2010-10-28

Read SwissProt file (1)

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The Read_SwissProt_file service reads the file at the swiss_prot_file_url and converts the SwissProt data into Agave format. The result is then sent to the workflow's swiss_prot_data port.

Created: 2010-10-28 | Last updated: 2010-10-28

Read GenBank file (1)

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The Read_GenBank_file service reads the file at the genbank_file_url and converts the GenBank data into Agave format. The result is then sent to the workflow's gen_bank_data port.

Created: 2010-10-28 | Last updated: 2010-10-28

Encode byte[] to base64 (1)

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The encode byte[] to base 64 service encodes the bytes representing (in the local character set) 'hello'.

Created: 2010-10-28

Decode base64 to byte[] (1)

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The decode base64 to byte[] service decodes the base64 string. The byte array is then converted into the string 'Hello world'.

Created: 2010-10-28

Is Parameter Optimization Useful (1)

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This process compares two 10x10fold cross-validations with linear SVM for classification. One use the heuristic parameter choice C=1/avg(K(x,x)) and the other uses an internal 5fold cross-validation to tune the parameter C. Afterwards both performance values are compared via ANOVA and T-test to check whether there is a significant difference between both performance values. The behaviour of the optimized 10x10 XV is similar to that of the experiment 10x10fold_XV_with_optimizedSVM_classificat...

Created: 2010-10-28 | Last updated: 2010-10-28

10x10fold_XV_with_optimizedSVM_classification (1)

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This process performs 10 round of 10fold cross-validation for classification with a linear SVM. In each traning-phase the parameters C of the linear SVM is tuned by a 5fold cross-validation. The repetions are useful for estimating the real performance on a small sample high variance data set, e.g. microarray data.

Created: 2010-10-28

Blastn-ClustalW (1)

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Created: 2010-10-27 | Last updated: 2010-10-27

Credits: Kalpana

Polygon buffer - Using pyWPS (1)

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Basic example on how to integrate a Web Processing Service (WPS) using  PyWPS's WSDL/SOAP The workflow will accept a GML file with featureCollection where polyon is described using gml:coordinate element structure. This GMl is easely generated with GDAL. The coodinate structure will be converted to WKT and used to create a polygon using shapely, the output will be a buffer polygon with a defined width. Output polygon will replace the orginal polygon and the existing XML will be return...

Created: 2010-10-26 | Last updated: 2010-10-26

Credits: Jorgejesus

Concatenate Examples (1)

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This process I'm using for preprocessing is concatenating the values of att2 for all the same values of att1 in att3. ps: Thanks to Matthias for his help.

Created: 2010-10-26 | Last updated: 2010-10-26

X-Validation with One-Class SVM (1)

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With this example process, I intend to show how one might validate predictions done with a one class classifier. RapidMiner includes the One-Class SVM (part of libsvm) as introduced by Schoelkopf. I'm very interested in feedback concerning problems with or errors in this experiment. Note that the data set (Sonar) is just a toy data set chosen for demonstration - learning a one class classifier on it won't give you any good results! DESCRIPTION The basic idea is to partition the data set...

Created: 2010-10-20 | Last updated: 2010-10-20

Find best feature selection method. (1)

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This processes searches for the best method for feature selection and the best feature subset size. Inside a parameter loop the number of selected features and the feature selection methods are evaluated with a 10fold cross-validation. The Feature Selection Extension ( https://sourceforge.net/projects/rm-featselext ) is required. BUGFIX: Please note that the plugin downloaded before 5th November contains a bug. Due to a bug in RM the TopK-operator sorted the features in the wrong direction. ...

Created: 2010-10-19 | Last updated: 2010-12-10

Recursive Feature Elimination with fully o... (1)

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The Process shows how you can use the RecursiveFeatureElimination-Operator of the Feature Selection Extension ( https://sourceforge.net/projects/rm-featselext) to select relevant features from a highdimensional dataset.

Created: 2010-10-19 | Last updated: 2010-12-10

Automatical Disabling / Enabling of Operat... (1)

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This meta process shows another possibility for automatically optimizing the process layout. The operator "OperatorEnabler" can be used to enable or disable one of its children. Together with one of the parameter optimization operators this can be used to check which operators should be used for optimal results. This is especially useful in order to determine which preprocessing operators should be used for a particular data set - learner combination or if you want to automatically test diffe...

Created: 2010-10-13

test P-GRADE wf (1)

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 This workflow serves only testing purposes.

Created: 2010-10-12 | Last updated: 2010-10-12

Credits: Pgtest

BioXSD example workflow (1)

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An attempt to implement in the BioXSD workflow described by Kalas et al., (2010) Bioinformatics 26:i540-6. The workflow only calls one BioXSD web service (BLAST) since the second service (ClustalW) in the example workflow is currently down :(

Created: 2010-10-12 | Last updated: 2010-10-12

Credits: Peter Li

Extended Operations for Nominal Values (1)

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This process shows examples for the extended operations for nominal values coming with one of the next RapidMiner updates (5.0.011 or 5.1.000). The operations are performed with the operator "Generate Attributes" and can be used directly within the expressions for the new attributes. The supported functions include Number to String [str(x)], String to Number [parse(text)], Substring [cut(text, start, length)], Concatenation [concat(text1, text2, text3...)], Replace [replace(text, what, by)],...

Created: 2010-10-05

DNA Shuffling Template - part 1: DNaseI di... (1)

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From Cameron Neylon's Bio-Blog a template for recording the data from a DNA shuffling experiment

Created: 2010-10-04 | Last updated: 2010-10-08

Credits: http://thebluerhino.myopenid.com/

Blastn-ClustalW (1)

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Created: 2010-09-29

Credits: Kalpana