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--- MATLAB/OCTAVE interface of MIML-DML ---

Table of Contents

• Introduction
• Installation
• Usage
• Returned Model Structure
• Copyright Notice

Introduction

This tool provides the matlab code for Multi-Instance Multi-Label Distance Metric Learning for Genome-Wide Protein Function Prediction.It is very easy to use.

Installation

The CVX toolbox is a necessary component for this code. CVX can be download from: http://cvxr.com/cvx/download/.

Example: matlab> cvx_setup matlab> cvx_startup

Usage

matlab> result=demo;

Result of Prediction

The function 'MIML-DML' return a struct "result" which store the predict reslut. The result has detailed outputs: [HammingLoss,RankingLoss,OneError,Coverage,Average_Precision,Average_Recall,Average_F1,Outputs,Pre_Labels,time]

• Outputs: output vector of our algorithm.
• Pre_Labels: predicted label vector of our algorithm.
• time: average runtime of this code.
• HammingLoss
• RankingLoss
• OneError
• Coverage
• Average_Precision
• Average_Recall
• Average_F1

Copyright Notice

This procedure is for scientific use only and can not be used for commercial purposes. If you are going to use the program in a paper or work, please quote the following paper.

Please refer to the following papers:

1. Xu, Y., et al. "Multi-instance multi-label distance metric learning for genome-wide protein function prediction." Computational Biology & Chemistry 63.C(2016):30-40.
2. Y. Xu et al., "A Unified Framework for Metric Transfer Learning," in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 6, pp. 1158-1171, June 1 2017. doi: 10.1109/TKDE.2017.2669193

For any question, please contact Yonghui Xu xu.yonghui@hotmail.com