myExperiment - Workflows

Taverna 2

Uploader

Wei Tan

Lymphoma type prediction based on microar... (7)

Download

Scientific value Using gene-expression patterns associated with DLBCL and FL to predict the lymphoma type of an unknown sample. Using SVM (Support Vector Machine) to classify data, and predicting the tumor types of unknown examples. Steps Querying training data from experiments stored in caArray. Preprocessing, or normalize the microarray data. Adding training and testing data into SVM service to get classification result.

Created: 2010-05-11 | Last updated: 2010-05-11

Credits: Wei Tan Ravi Stian Soiland-Reyes

Taverna 2

Uploader

Stian Soiland-Reyes

Using CQL to query protein sequence data (1)

Download

To query protein sequence infomation out of 3 caGrid data services: caBIO, CPAS and GridPIR Adapted from http://www.myexperiment.org/workflows/600

Created: 2009-05-07

Credits: Stian Soiland-Reyes

Attributions: Using CQL to query protein sequence data

Taverna 1

Uploader

Wei Tan

Using CQL to query protein sequence data (1)

Download

To query protein sequence infomation out of 3 caGrid data services: caBIO, CPAS and GridPIR. Scientific value To query protein sequence information out of 3 caGrid data services: caBIO, CPAS and GridPIR. To analyze a protein sequence from different data sources. Steps Querying CPAS and get the id, name, value of the sequence. Querying caBIO and GridPIR using the id or name obtained from CPAS.

Created: 2008-12-05 | Last updated: 2009-07-14

Credits: Wei Tan

Taverna 1

Uploader

Wei Tan

hierarchical microarray clustering (1)

Download

To illustrate our caGrid plug-in’s application, we tested it with a microarray hierarchical clustering workflow that involves services hosted at multiple institutions. Microarrays are a high-throughput technology used to measure the expression of tens of thousands of genes in different tissues or cells. Scientists represent the data from each microarray via a vector (profile) in which each element represents a gene’s expression level. They use clustering analysis to identify sim...

Created: 2008-12-05 | Last updated: 2008-12-05

Credits: Wei Tan

Taverna 2

Uploader

Wei Tan

Support-Vector-Machine (SVM) based data cl... (2)

Download

Support-Vector-Machine based data classificationSupport-Vector-Machine based data classification using genePattern SVM service, the input should be in genePattern STATML format.

Created: 2010-05-24 | Last updated: 2010-05-24

Credits: Wei Tan

Taverna 1

Uploader

Wei Tan

caDSR metadata query in caGrid (1)

Download

This workflow shows the coordinated use of two services in CaGrid, i.e., the caDSR (Cancer Data Standards Repository) and EVS (Enterprise Vocabulary Services) services. caDSR is to define a comprehensive set of standardized metadata descriptors for cancer research terminology used in information collection and analysis. EVS provides resources and services to meet NCI needs for controlled terminology, and to facilitate the standardization of terminology and information systems across the Insti...

Created: 2008-12-05 | Last updated: 2008-12-05

Credits: Wei Tan

Taverna 2

Uploader

Wei Tan

genePattern data preprocessing (2)

Download

preprocess data set using genePattern preProces service, the input should be in genePattern STATML format. Configuration parameters can be adjusted by changing the default preprocess data set using genePattern preProces service, the input should be in genePattern STATML format.preprocess data set using genePattern preProces service, the input should be in genePattern STATML format. Configuration parameters can be adjusted by changing the string constants.

Created: 2010-05-24 | Last updated: 2010-05-24

Credits: Wei Tan

Taverna 2

Uploader

Wei Tan

caArray data retrieving (1)

Download

Query all the gene expression data in a caArray experiment. Returns a evenly divided gene expression data set with corresponding class information. They ca be later used as training and test data set in many classification algorithms.Query all the gene expression data in a caArray experiment. Returns a evenly divided gene expression data set with corresponding class information. They can be later used as training and test data set in many classification algorithms.