# MLRDFM

**Repository Path**: Tomhappy/MLRDFM

## Basic Information

- **Project Name**: MLRDFM
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-10-22
- **Last Updated**: 2023-10-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MLRDFM
This project includes a Tensorflow implementation of a Multi-view Laplacian regularized DeepFM model for miRNA-disease association prediction
 
The code is for the Paper "MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations".
If you use this code, please cite this paper.


# ENVIRONMENT
Python 3.6.13

Tensorflow 1.14

numpy 1.17.0

scipy 1.5.2




# Usage
## Input Format
This implementation requires the input data in the following format:
- [ ] **Xi**: *[[ind1_1, ind1_2, ...], [ind2_1, ind2_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]*
    - *indi_j* is the feature index of feature field *j* of sample *i* in the dataset
- [ ] **Xv**: *[[val1_1, val1_2, ...], [val2_1, val2_2, ...], ..., [vali_1, vali_2, ..., vali_j, ...], ...]*
    - *vali_j* is the feature value of feature field *j* of sample *i* in the dataset
    - *vali_j* can be either binary (1/0, for binary/categorical features) or float (e.g., 10.24, for numerical features)
- [ ] **y**: target of each sample in the dataset (1/0 for classification, numeric number for regression)