Drug repositioning through integration of prior knowledge and projections of drugs and diseases
| Autor: | Tiangang Zhang, Xiao Wang, Ping Xuan, Shuxiang Pan, Yangkun Cao, Tonghui Shen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Drug Drug-Related Side Effects and Adverse Reactions Computer science media_common.quotation_subject 0206 medical engineering 02 engineering and technology Disease Computational biology Biochemistry 03 medical and health sciences Similarity (psychology) Molecular Biology 030304 developmental biology media_common 0303 health sciences Therapeutic effect Drug Repositioning Computational Biology Computer Science Applications Computational Mathematics Drug repositioning Computational Theory and Mathematics Drug development 020602 bioinformatics Algorithms |
| Zdroj: | Bioinformatics (Oxford, England). 35(20) |
| ISSN: | 1367-4811 |
| Popis: | Motivation Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases from multiple sources for predicting the candidate drug–disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations. Results We present a method based on non-negative matrix factorization, DisDrugPred, to predict the drug-related candidate disease indications. A new type of drug similarity is firstly calculated based on their associated diseases. DisDrugPred completely integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different levels including the chemical structures of drugs, the target proteins of drugs, the diseases associated with drugs and the side effects of drugs. The prior knowledge of drugs and diseases and the sparse characteristic of drug–disease associations provide a deep biological perspective for capturing the relationships between drugs and diseases. Simultaneously, the possibility that a drug is associated with a disease is also dependant on their projections in the low-dimension feature space. Therefore, DisDrugPred deeply integrates the diverse prior knowledge, the sparse characteristic of associations and the projections of drugs and diseases. DisDrugPred achieves superior prediction performance than several state-of-the-art methods for drug–disease association prediction. During the validation process, DisDrugPred also can retrieve more actual drug–disease associations in the top part of prediction result which often attracts more attention from the biologists. Moreover, case studies on five drugs further confirm DisDrugPred’s ability to discover potential candidate disease indications for drugs. Availability and implementation The fourth type of drug similarity and the predicted candidates for all the drugs are available at https://github.com/pingxuan-hlju/DisDrugPred. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|