Copyright (C) 2014 Jianxin Wang(jxwang@mail.csu.edu.cn),Huimin Luo(luohuimin@csu.edu.cn)

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program; if not, see http://www.gnu.org/licenses/.

Jianxin Wang(jxwang@mail.csu.edu.cn),Huimin Luo(luohuimin@csu.edu.cn) School of Information Science and Engineering Central South University ChangSha CHINA, 410083

MBiRW is one novel computational method, which utilizes comprehensive similarity measures and Bi-Random walk algorithm to identify potential novel indications for a given drug.

1.Dataset.

1. DrugSimMat and DiseaseSimMat store drug similarity matrix and disease similarity matrix, respectively;

2. DiDrAMat stores known disease-drug association information;

3. DrugsName and DiseasesName store drug ids and disease ids, respectively;

4. For each drug pair, the number of their sharing common diseases is stored in shareWrr.mat;

5. For each disease pair, the number of their sharing common drugs is stored in shareWdd.mat;

6. CDataSets store the combined datasets; Datasets_indep store the independent dataset.

2.Code.

1. normFun.m: function implementing normalization;

2. setparFun.m: function analyzing similarity network;

3. nManiCluester.m : function implementing cluster operation by calling cluster_one-1.0;

4. MBiRW: predict potential indications for drugs;

All files of Dataset and Code should be stored in the same folder to run MBiRW.