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Items tagged with "lymphoma workflow" (4)

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Files (3)
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Blob Input CQL of the lymphoma prediction workflow

Created: 23/04/09 @ 17:25:04 | Last updated: 03/09/11 @ 21:57:43

Credits: User Wei Tan

Attributions: Workflow Lymphoma type prediction based on microarray data

License: Creative Commons Attribution-Share Alike 3.0 Unported License

Input CQL of the lymphoma prediction workflow at http://www.myexperiment.org/workflows/746?version=1. It is only for the purpose of illustration and  NOT needed to run the workflow since the input has already been embedded.

File type: XML

Rating: 0.0 / 5 (0 ratings) | Comments: 0 | Viewed: 23 times | Downloaded: 15 times

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Uploader

Blob Output of the lymphoma prediction workflow

Created: 23/04/09 @ 17:37:16 | Last updated: 03/09/11 @ 21:56:29

Credits: User Wei Tan

Attributions: Workflow Lymphoma type prediction based on microarray data

License: Creative Commons Attribution-Share Alike 3.0 Unported License

Output of the lymphoma prediction workflow at http://www.myexperiment.org/workflows/746?version=1. It shows the lymphoma type prediction results. It contains lymphoma type predictions for 38 samples and there is only one error (on testSample_4).

File type: XML

Rating: 0.0 / 5 (0 ratings) | Comments: 0 | Viewed: 18 times | Downloaded: 18 times

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Uploader

Blob Output of the lymphoma prediction workflow (on Ubunt...

Created: 23/04/09 @ 20:06:51

Credits: User Wei Tan

License: Creative Commons Attribution-Share Alike 3.0 Unported License

testing on Ubuntu Linux 8.0.4 + Java 1.5_16

File type: XML

Rating: 0.0 / 5 (0 ratings) | Comments: 0 | Viewed: 29 times | Downloaded: 19 times

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Workflows (1)
Original Uploader

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

Created: 11/05/10 @ 19:04:30 | Last updated: 11/05/10 @ 19:04:32

Credits: User Wei Tan User Ravi User Stian Soiland-Reyes

License: Creative Commons Attribution-Share Alike 3.0 Unported License

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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.

Rating: 5.0 / 5 (1 rating) | Versions: 7 | Reviews: 0 | Comments: 0 | Citations: 3

Viewed: 685 times | Downloaded: 254 times

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