SVM SeqClassifier 1.2: Train Your Sequences To Learn
| Support Vector Machine (SVM) is one of the
best statistical learning methods. The SVM Classifier 1.2 and
SVM SeqClassifier 1.2 are based on JAVA Implementation of LIBSVM,
a simplified form of SVM.
The goal is to provide biologists
a friendly tool to test their hypothesis without the need of programming. Users may find the light version
satisfactory for many applications.
SeqClassifier --- DNA / Protein sequence data
SVM Classifier --- numerical data.
Order SVM SeqClassifier
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Mac: Internet Explorer 5.0, IE5.1.4 or higher (IE5.1 will not work because of a bug.)
Need to raise a better antibody? |
Please use online Abie Pro 3.0 for
peptide antibody design.
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Example III: Prediction of Splice Sites
Test Drive SVM SeqClassifier 1.2|
1. Click here to download the training data.
2. Select the data, copy (Ctrl-c or Apple-c) and paste (Ctrl-v or Apple-v)
into the training data input window (top-left) of the SVM SeqClassifier 1.2.
The input data file should have the following format:
3. Select model, kernel, and whether to scale the data. If the scale is checked, each
row will be scaled internally (should not make big differences for SeqClassifier whether the data is scaled or not).
4. Click on the Train button to train a model.
5. Click here to download the testing data.
6. Select the data, copy (Ctrl-c or Apple-c) and paste (Ctrl-v or Apple-v)
into the testing data input window (top-right) of the SVM SeqClassifier 1.2.
7. Click on the Predict button to predict.
8. Click on the Validate button to run cross-validation.
Warning: Due to limit of your clip board memory, it is impossible to copy/paste a large dataset.
You would need the Stand-alone version, which allows data upload.
The data for this demo is the human 5'-splice site
used in the GENIE system. Additional data can be found at
Berkeley Drosophila Genome Project.
For the demo purpose, the first 100 true and first 200 false 5'-donor sequences from training or testing sets are used.
Example IV: Just for Fun
SVM SeqClassifier is designed as a flexible, light-weight and user-friendly Classifier.
For most dataset with tens to hundreds of training and testing sequences, it should
finish reasonably fast on personal computers. For large dataset with thousands of sequences,
SeqClassifier may need to be modified. Please contact us for details.
Copy and paste the "omics" data into the SeqClassifier, and try to find the
the best model for predicting next "true omics" related to biology.
Example V: siRNA Design
Click here to see how SVM can be applied to
select for siRNA.
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