# DTINet

**Repository Path**: uncle-mo/DTINet

## Basic Information

- **Project Name**: DTINet
- **Description**: A Network Integration Approach for Drug-Target Interaction Prediction
- **Primary Language**: Matlab
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-10-09
- **Last Updated**: 2022-10-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## DTINet: A Network Integration Approach for Drug-Target Interaction Prediction
DTINet is a computational pipeline to predict novel drug-target interactions (DTIs) from heterogeneous network. DTINet focuses on learning a low-dimensional vector representation of features for each node in the heterogeneous network, and then predicts the likelihood of a new DTI based on these representations via a vector space projection scheme. See our paper on [Nature Communications](https://www.nature.com/articles/s41467-017-00680-8) and preprint on [bioRxiv:100305](http://www.biorxiv.org/content/early/2017/03/20/100305).

### Quick start
We provide an example script to run experiments on our dataset: 

- Run `run_DTINet.m`: predict drug-target interactions, and evaluate the results with cross-validation. 

**Note:** See the "Tutorial" section below for a detailed instruction on how to specify parameters of DTINet, or how to run DTINet on your own dataset.

### Supplementary Information
#### `supplementary/` directory
- `Supplementary_Data_1.xlsx`:  The list of top 150 novel drug-target interactions predicted by DTINet, which was trained based all on drugs and targets that have at least one known interacting pair. Known drug-target pairs (corresponding to those non-zero entries in the drug-target interaction matrix) and novel predicted DTIs that share homologous proteins (with sequence identity scores >40%) with known DTIs were excluded from the list.
- `Supplementary_Data_2.xlsx`:  The entire list of novel drug-target interactions predicted by DTINet, which was trained based on all drugs and targets that have at least one known interacting pair.
- `Supplementary_Data_3.xlsx`:  Examples of the novel predictions which can be supported by the previous known evidence in the literature.

### Code and data
#### `src/` directory
- `DTINet.m`: predict drug-target interactions (DTIs)
- `DCA.m`: compact feature learning by integrating heterogeneous network
- `diffusionRWR.m`: network diffusion algorithm (random walk with restart)
- `compute_similarity.m`: compute Jaccard similarity based on interaction/association network
- `auc.m`: evaluation script
- `run_DCA.m`: example code of running `DCA.m` for feature learning
- `run_DTINet.m`: example code of running `DTINet.m` for drug-target prediction
- `train_mf.mexa64`: pre-built binary file of inductive matrix completion algorithm (downloaded from [here](http://bigdata.ices.utexas.edu/software/inductive-matrix-completion/))
- `download_imc.sh`: download the inductive matrix completion source and build the executable library from source.

#### `data/` directory
- `drug.txt`: list of drug names
- `protein.txt`: list of protein names
- `disease.txt`: list of disease names
- `se.txt`: list of side effect names
- `drug_dict_map`: a complete ID mapping between drug names and DrugBank ID
- `protein_dict_map`: a complete ID mapping between protein names and UniProt ID
- `mat_drug_se.txt` 		: Drug-SideEffect association matrix
- `mat_protein_protein.txt` : Protein-Protein interaction matrix
- `mat_protein_drug.txt` 	: Protein-Drug interaction matrix
- `mat_drug_protein.txt` 	: Drug_Protein interaction matrix (transpose of the above matrix)
- `mat_drug_protein_remove_homo.txt`: Drug_Protein interaction matrix, in which homologous proteins with identity score >40% were excluded (see the paper).
- `mat_drug_drug.txt` 		: Drug-Drug interaction matrix
- `mat_protein_disease.txt` : Protein-Disease association matrix
- `mat_drug_disease.txt` 	: Drug-Disease association matrix
- `Similarity_Matrix_Drugs.txt` 	: Drug similarity scores based on chemical structures of drugs
- `Similarity_Matrix_Proteins.txt` 	: Protein similarity scores based on primary sequences of proteins
**Note**: drugs, proteins, diseases and side-effects are organized in the same order across all files, including name lists, ID mappings and interaction/association matrices.

#### `feature/` directory
We provided the pre-trained vector representations for drugs and proteins, which were used to produce the results in our paper.
- `drug_vector_d100.txt`
- `protein_vector_d400.txt`

### Third-party software
Our implementation requires the [Inductive Matrix Completion](http://bigdata.ices.utexas.edu/software/inductive-matrix-completion/) (IMC) library. We provide an executable binary file in the src/ folder for convenience. The executable binary file was built on a typical Ubuntu 14.04 (64 bit) system. If you are using other Linux platforms, please consider building the library from its source by running `bash download_imc.sh`.

**Tips**: We recommend users to download and install the IMC library using the `download_imc.sh` script. If you download the library yourself from the [website](http://bigdata.ices.utexas.edu/software/inductive-matrix-completion/) of IMC, please be aware that DTINet requires the C/C++ version (with Python and Matlab interfaces). Please do not use the other version, i.e., a pure MATLAB implementation. The pure MATLAB version treats the unknown/missing entries in the interaction matrix as zeros, which is not the same as required in DTINet.

### Tutorial
1. Put interaction/association matrices in the `data/` folder.
2. Create a `network/` folder under `DTINet/` and run `compute_similarity.m`, which will compute the Jaccard similarity of drugs and proteins, based on interaction/association matrices.
3. Specify parameters (number of dimensions of feature vectors, restart probability, the maximum number of iterations) and run `run_DCA.m`, which will learn the feature vectors of drugs and proteins and save them in the `feature/` folder.
4. Set the path of feature vectors and corresponding parameters in `run_DTINet.m` and execute it. This script will predict the drug-target interactions and evaluate the results using a ten-fold cross-validation.

### Citation
Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., Peng, J., Chen, L. & Zeng, J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. *Nature Communications* **8**, (2017).

    @article{Luo2017,
      author = {Yunan Luo and Xinbin Zhao and Jingtian Zhou and Jinglin Yang and Yanqing Zhang and Wenhua Kuang and Jian Peng and Ligong Chen and Jianyang Zeng},
      title = {A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information},
      doi = {10.1038/s41467-017-00680-8},
      url = {https://doi.org/10.1038/s41467-017-00680-8},
      year  = {2017},
      month = {sep},
      publisher = {Springer Nature},
      volume = {8},
      number = {1},
      journal = {Nature Communications}
    }

### Contacts
If you have any questions or comments, please feel free to email Yunan Luo (luoyunan[at]gmail[dot]com) and/or Jianyang Zeng (zengjy321[at]tsinghua[dot]edu[dot]cn).