SVM SeqClassifier 1.2: Train Your Sequences To Learn           Order Home

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 since 3/25/03

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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. Data 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

+1 --Chemomics +1 -proteomics +1 ---genomics +1 -Agronomics +1 --bionomics -1 -deadnomics -1 bushonomics -1 Reidonomics -1 Urbannomics -1 -EekANomics -1 -Ragonomics -1 -eekanomics -1 -firenomics -1 -ergonomics -1 -Oeconomics -1 Phoronomics -1 -Pyronomics -1 Loxodromics -1 Bubbleomics -1 -Enronomics -1 -optinomics -1 --economics -1 --netnomics -1 ARMEYNOMICS 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. 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.

Example V: siRNA Design

Click here to see how SVM can be applied to select for siRNA.

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