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Support Vector Machine (SVM Classifier 1.2): Learn to Classify           Order Home
   Support Vector Machine (SVM) is one of the best statistical learning methods. In many cases, its performance (i.e., error rate on test data) either matches or is significantly better than that of competing 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. See below for a few examples.
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SVM Classifier is now included in BioToolKit 300. Click here to download.New
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Support Vector Machines Online Machine Learning
Machine Learning Resources SVM Applications in Biology
Example I: SVM Classification of Tumor Types
Test Drive SVM Classifier 1.2
1. Click here to download the training data.
2. Select from the second line, copy (Ctrl-c or Apple-c) and paste (Ctrl-v or Apple-v) into the training data input window (top-left) of the SVM Classifier 1.2. Do not include the first line of column names.
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 such that Sxi2 = N. Here N is the number of data points in each row (vector).
4. Click on the Train button to train a model.
5. Click here to download the testing data.
6. Select from the second line, copy (Ctrl-c or Apple-c) and paste (Ctrl-v or Apple-v) into the testing data input window (top-right) of the SVM Classifier 1.2. Do not include the first line of column names.
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 microarray data for this demo is published in Ramaswamy et al. PNAS 98(26):15149-54. Supplement material and data can be found at Whitehead Institute Center for Genome Research.

For the demo purpose, the data was selected/filtered following the steps: (1) No additional normalization; (2) Select top candidates in OVA; (3) Data values smaller than 10 are raised to 10 and data values greater than 5000 are lowered to 5000; (4) Select features with median expression levels (i.e., the total expression in the training set is 10000 ~ 30000 for each feature).

Figure 2. Prediction and cross-validation accuracy. Features are selected from the training data (144 primary tumors) and the test set (46 primary tumors, excluding metastatic) as described in the above. Training, prediction, and 5-fold cross-validation used the C-SCV model and linear kernel. The data is scaled such that Sxi2 = N. The stand-alone version is needed for feature numbers greater than 100 because of the limit on clip-board memory.

A typical training and testing data with ~300 features can be downloaded here: Training Test. Please remove the first line of feature names before loading the files.

Example II: SVM Classification of Drugs Based On the Mechanism of Actions (MOAs)
Click here for the drug activity data of 115 drugs against a panel of 60 cell lines (A matrix, excluding 3 drugs with unknown MOA)2. For details, please visit authors web page.

Drugs with different mechanism of actions have different profiles of drug activities against the 60 cell lines. Giving the drug activities, we may also predict a drug's mechanism of actions: Input the data into SVM, one should find the 5-fold validation accuracy is greater than 70% (C-SVC model, polynomial kernel, scale data). Note SVM will predict only MOAs present in the data set. For this reason, the prediction accuracy may be low for a multiclass classifier. The performance can be improved if we are only interested in known whether a drug belongs to one of the classes. In the latter case, we may divide the training set into two classes --- drugs that belong or do not belong to the class to be tested.

Example III: SVM Classification of DNA or Protein Sequences
SVM SeqClassifier is an extension of SVM Classifier: SeqClassifier accepts inputs of DNA or protein sequences. SeqClassifer can be trained to recognize cis-controling sequences (e.g., splicing site, promoter, translation initiation sequence, protein binding sequences, etc.) and protein motifs (e.g., protease sites, phophorylation sites, localization signals, interaction domains, etc.). Click here to test it.
Example V: siRNA Design
Click here to see how SVM can be applied to select for siRNA.
Example VI: Protein Cellular Localization
Click here to download training/testing data for protein cellular localization predictions. Chose a small data set, you will not be able to copy/paste a large data set. The data is in svm-light format that can be accepted by this online tool. Try to change model/kernel to find the best prediction accuracy.
References
1. Multiclass cancer diagnosis using tumor gene expression signatures. Ramaswamy et al., Proc Natl Acad Sci U S A. 2001 Dec 18;98(26):15149-54.
2.A gene expression database for the molecular pharmacology of cancer. Scherf et al., Nature Genetics 2000; 24: 236-244.
3. For more information about limsvm, please visit the home page of Dr. Chih-Jen Lin.
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