The following files can be downloaded within svmPRAT's distribution:
EXECUTABLES | ||
svmPRAT_SunOS-sun4u.zip | This contains SUN Solaris binaries optimized with & without the blas libraries compiled on Sun-Blade-1500 Solaris. | |
svmPRAT_Linux-i686.zip | This contains the 32-bit Linux binaries optimized with & without the blas libraries compiled on i686 Intel(R) Pentium(R) 4 CPU. | |
svmPRAT_Linux-x86-64.zip | This contains the 64-bit Linux binaries optimized with & without the blas libraries compiled on Dual Core AMD Opteron(tm) Processor 270. | |
svmPRAT_Darwin-i386.zip | This contains the 32-bit DARWIN (Mac OS X 10.5.3) binaries optimized with & without the blas libraries compiled on Intel Core Duo Processors. | |
svmPRAT_MSWIN-x86.zip | This contains the 64-bit MS WIN compiled without the blas libraries compiled on Intel Core 2 Duo Processors using cygwin and Microsoft Visual Studio. svmPRAT_Eval will be added soon to this folder. Note you will need cygwin to be installed on your system to use these binaries. | |
EXAMPLE FILES | ||
TOY Data | The zip file provides a set of profiles (PSSMS) for 10 sequences that are used for training and another 10 sequences that are used for testing. The true labels are provided for the training sequences and they include whether a residue is disorder or not. Steps to check your binaries. 1. Unzip toy_data.zip. 2. Go to the folder toy_data. 3. The files "train_10.lst" and "test_10.lst" contain the listing of the train and test files. The files ending with suffix "pssm.new" are the PSI-BLAST profile files and the files ending with suffix "disnew" are the true labels for the disorder prediction problem. 4. To run the learn program /path-where-you-extracted-svmPRAT/svmPRAT_Learn1.0 train_10.lst model-name . To run the prediction program /path-where-you-extracted-svmPRAT/svmPRAT_Predict1.0 test_10.lst model-name prefix-name . To run the evaluation program ./path-where-you-extracted-svmPRAT/svmPRAT_Eval1.0 train_10.lst
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Usage |
svmPRAT_Learn [options] <input file> <model file> |
Input |
svmPRAT_Learn has two required parameters:
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Output |
The output is a model file which contains information regarding the models learned and stored, which is also used as input to the svmPRAT_Predict program. |
Options |
-wmer=<integer>
Specifies the length of the wmer that should be used for a feature
for a residue. In particular, features are generated using wmer residues
to the left, wmer residues to the right and the central residue
(2w+1) residues. Default value is wmer=2
-kernel={custom,linear,quad,soe,rbf}
Specifies the kernel to be used for svm-light. The possible values are:
-c=<float>
custom User defined custom kernel. linear Linear dot product kernel. quad Quadratic kernel. soe Normalized second-order exponential kernel (Default). rbf Standard radial basis kernel function.
The regularization parameter provided to SVM learning. It
controls trade-off between training error and margin. Default is 0.1
-smer=<float>
Specifies the length (< wmer) upto which the sequence residues contribute all their feature weights. Residues that are < wmer and > smer
are averaged out.
-usecr
Cost Ratio flag is turned on with this parameter. Used when data has
uneven distribution of classes. Enables a cost factor by
which training errors on positive examples outweigh errors on negative
examples (default 1 when the flag is off).
-cascade=<float>
This is used to invoke/learn a cascaded level model, where features
are derived by building a first level model. The floating point allows
setting up weight for the predictions from first-level model for
the second-level model.
-help
Prints the above help message.
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Usage |
svmPRAT_Predict <test file> <model file> <prediction file> |
Input |
svmPRAT_Predict has three required parameters:
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Output |
The output consists of a set of predicted annotations and profiles, which are nothing but the SVM predictions from each of the "num element" SVM models. An example of an output profile for an annotation containing 16 elements is shown below:
Example of predicted annotation file for a protein:
000 14 -1.0004 -1.0592 -1.1488 -1.0352 -1.2810 -1.3322 -1.2483 -1.4799 -1.2799 -1.3312 -1.3516 -1.2172 -0.9993 -1.0002 +0.9993 -1.1071 001 14 -1.5512 -1.0308 -1.1452 -1.0157 -1.1897 -1.2397 -0.9997 -1.1835 -1.2996 -1.3314 -1.2970 -1.5036 -1.0006 -1.1929 +0.9997 -1.5617 002 14 -0.9995 -1.0573 -1.2493 -1.0456 -1.1514 -1.2316 -1.0008 -1.0006 -1.3156 -1.1864 -1.6694 -1.0069 -1.0898 -1.5569 +0.9999 -1.4992 003 14 -1.4105 -1.0994 -1.2708 -1.0072 -1.2759 -1.0005 -0.9995 -1.0840 -1.2864 -0.9999 -1.4743 -1.0155 -0.9995 -1.3479 +0.9995 -1.0651 004 11 -1.2225 -1.0916 -1.1872 -1.0281 -1.2961 -1.1643 -1.1118 -1.1245 -1.1537 -1.3704 -1.3907 +0.9999 -0.9996 -1.0004 -0.9999 -1.2140 005 12 -0.9995 -1.3154 -1.1994 -1.0214 -1.2783 -1.1586 -1.1875 -1.4170 -1.2909 -1.3497 -1.6379 -0.9993 +1.0001 -1.4284 -0.9998 -1.0981 006 12 -1.6333 -1.1797 -1.3248 -1.0424 -1.1269 -1.1318 -1.2340 -1.3809 -1.2517 -1.2218 -1.2112 -0.9995 +1.7395 -1.2302 -1.6457 -1.4433 007 12 -1.4013 -1.0892 -1.2256 -1.0161 -1.3483 -1.4000 -1.0078 -1.2499 -1.1480 -1.4589 -1.1845 -1.6315 +1.3110 -1.0090 -1.7057 -1.2312 008 12 -1.4578 -1.2840 -1.2500 -1.0495 -1.2260 -1.2836 -1.6059 -1.2687 -1.2792 -1.3255 -1.3207 -1.0484 +1.0001 -1.4365 -1.0002 -1.2322 009 12 -1.5083 -1.2800 -1.3130 -1.0532 -1.0515 -1.4708 -1.8131 -1.6142 -1.2192 -1.4026 -1.3504 -1.2876 +1.0007 -1.1374 -0.9997 -1.2064 010 12 -0.9998 -1.0003 -1.2860 -1.0487 -1.2841 -1.2959 -0.9997 -1.4049 -1.2458 -1.3730 -1.3588 -1.3606 +0.9995 -1.5204 -0.9994 -1.4352 011 12 -1.3758 -1.1462 -1.3136 -1.0445 -1.0247 -1.5192 -0.9995 -1.5453 -1.2707 -1.2739 -1.1607 -1.4221 +0.9999 -0.9998 -1.0007 -1.4906 012 6 -0.9994 -1.1934 -1.2947 -1.0490 -1.2755 -1.3801 +0.9994 -1.4202 -1.0704 -1.3127 -1.5102 -1.5656 -0.9995 -1.0937 -0.9994 -1.0534 013 14 -1.3359 -1.2518 -1.2288 -1.0437 -1.2852 -1.2509 -0.9997 -1.1385 -1.2780 -1.4604 -1.3463 -1.6658 -1.2496 -1.0711 +1.3739 -1.1819 014 15 -1.3467 -1.2115 -1.1412 -1.0403 -1.1839 -1.4935 -1.0000 -0.9993 -1.2805 -1.3932 -1.3356 -1.2321 -2.0749 -1.0000 -1.0001 +0.9993 015 7 -1.0002 -1.2452 -1.1169 -1.0453 -1.2380 -1.0668 -0.9995 +1.0000 -1.1621 -1.3070 -1.0004 -0.9993 -1.5969 -0.9998 -0.9998 -1.0000 016 14 -1.2752 -1.1088 -1.0005 -1.0451 -1.1178 -1.0702 -1.0005 -0.9997 -1.1890 -1.3662 -1.4341 -1.0004 -1.5173 -0.9997 +0.9995 -1.0825 ........ ..... The first column gives the residue number. The second column gives the predicted annotation label. The sixteen columns represent the profile and are predictions from the 16 SVM models in this case. |
Options |
No Extra optional parameters for now. All are dependent on the training or svmPRAT_Learn parameters. |
Usage | |||
svmPRAT_Eval [options] <input file> | |||
Input | |||
svmPRAT_Eval has one required parameters:
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In citing svmPRAT in your papers, please use the following reference:
The software may not be sold or redistributed without prior approval. One may make copies of the software for their use provided that the copies, are not sold or distributed, are used under the same terms and conditions.
As unestablished research software, this code is provided on an ``as is'' basis without warranty of any kind, either expressed or implied. The downloading, or executing any part of this software constitutes an implicit agreement to these terms. These terms and conditions are subject to change at any time without prior notice.