cinkkkyo / mdnnmd

An implementation of multimodal deep neural network, a new model for human breast cancer prognosis prediction.

MDNNMD

An implementation of multimodal deep neural network, a new model for human breast cancer prognosis prediction.

Reference

Our manuscipt titled with "A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data" has been accepted by IEEE/ACM Transactions on Computational Biology and Bioinformatics. If you find MDNNMD useful in your research, please consider citing:

Sun, D., Wang, M., & Li, A. (2018). A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Requirements

[python 2.7](https://www.python.org/downloads/)
[TensorFilow 1.0](https://www.tensorflow.org/install/)
[scikit-learn 0.18](http://scikit-learn.org/stable/)
[cuda 8.0](https://developer.nvidia.com/cuda-downloads)

Usage

python MDNNMD.py

Parameters of MDNNMD

The Parameters of MDNNMD are in our configuration file mdnnmd.conf. The descriptions of these parameters of MDNNMD are provided below:

=================================================================================================
| PARAMETER NAME       | DESCRIPTION                                                            |
=================================================================================================
|       α,β,γ          |α,β,γ are three damping factors used to balance the contribution for    |
|                      |each DNN model. Here the sum of three damping factors should be equal 1.|
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|         K            |the number of fold with cross validation experiment or an index file.   |
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|         D1           |the data file of gene expression profile                                |
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|         D2           |the data file of copy number alteration profile.                        |
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|         D3           |the data file of clinical information                                   |
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|        LABEL         |the predict label of breast cancer patients with 1 or 0.                |
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|      batch_size      |mini-batch size.                                                        |
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|         bne          |batch normalization epsilon.                                            |
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|   active_function    |active_function in our MDNNMD model, choose tanh or relu.               |
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Output files of MDNNMD

The descriptions of output files of MDNNMD are provided below:

====================================================================================================================
| VARIABLE NAME         |                                   DESCRIPTION                                            |
====================================================================================================================
| Prediction_score.txt  |The final prediction score of all samples with 10 fold cross validation experiment.       |
|                       |The output of the MDNNMD with a softmax function.                                         |
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| Prediction_labels.txt |The prediction labels represent long-term patients with 0 and short-term patients with 1. |
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Contact

Author: Dongdong Sun @HILAB
Maintainer: Dongdong Sun
Mail: [email protected] Date: 2017-5-30
Health Informatics Lab, School of Information Science and Technology, University of Science and Technology of China