Machine Learning-Based pre-miRNA Sequence Classifier

This Matlab software is an easy-to-use tool for predicting pre-miRNA sequences via automatic feature extraction and SVM model selection. It also provides an interface to Web-demo builder for creating a Web interface.

This software is being developed at the sinc(i) lab as a requirement for obtainng an IT Engineering degree at FICH-UNL, Santa Fe, Argentina.

Getting started

Software requirements

For SVM classification, the software requires either LIBSVM (recommended) or Matlab's Bioinformatics Toolbox. For MLP classification, support for Matlab's Neural Network Toolbox (recommended) and FANN is available. Parallel Computing Toolbox is also supported for speeding up the training process.

Setup

In a Debian GNU/Linux system (stable), follow the steps

1. Install required tools

    sudo aptitude install git build-essential
   

2. Check out the code

    git clone https://github.com/maurete/pfc.git
   

3. In Matlab prompt, cd to the src/ folder and run setup script

    cd pfc/src setup
   

   If you get an error building the FANN library, please ignore it: FANN support is somewhat experimental and very slow. For now, use of Matlab's Neural Network Toolbox is strongly advised. Better FANN support might be available in future versions.

4. If you are planning to work with plain (i.e. "unfolded") FASTA files, you are strongly advised to install the Vienna RNA Package for extracting secondary structure information. Follow the link for downloading and installing instructions for your system.

5. If all went well, you are now ready to start using the software.

Using the software

Matlab command prompt

Once required software is set up, you will be able to invoke the program functions inside Matlab by doing cd to the src directory. The main workflow for using the software is a three-step process:

1. Generate classification problem: use the problem_gen function for generating the structure describing the classification problem at hand, including train and test datasets. Type help problem_gen in the Matlab prompt for further details on using this function.

2. Build classifier model: the function select_model lets you obtain optimal parameters and train the classifier for the specified problem. You can get help on using this function by typing help select_model in Matlab.

3. Classify test datasets: you can perform classification on the test dataset for a problem by invoking the function problem_classify which receives a problem like the one generated in step 1 and the model obtained from step 2 as arguments. See the built-in help for this function by invoking help problem_classify within Matlab.

Web demo interface

Besides the command prompt interface, you can build a basic web interface for the program with the help of Web-demo builder. Typing make in a shell (not Matlab) prompt inside the src directory will create a .zip file suitable for uploading to a Web-demo builder instance. Once you uload the file to Web-debo builder, follow the assistant by selecting webif as the main function and setting up each parameter type and options. More information on what these parameters mean can be obtained by typing help webif in your Matlab environment.