Inferring drug-disease associations by a deep analysis on drug and disease networks
Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug reposi...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-06-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023632?viewType=HTML |
| _version_ | 1797765728380649472 |
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| author | Lei Chen Kaiyu Chen Bo Zhou |
| author_facet | Lei Chen Kaiyu Chen Bo Zhou |
| author_sort | Lei Chen |
| collection | DOAJ |
| description | Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes. |
| first_indexed | 2024-03-12T20:14:16Z |
| format | Article |
| id | doaj.art-0e0d9514e78e45f2a96c61cd6ea66156 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-12T20:14:16Z |
| publishDate | 2023-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-0e0d9514e78e45f2a96c61cd6ea661562023-08-02T01:24:49ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-06-01208141361415710.3934/mbe.2023632Inferring drug-disease associations by a deep analysis on drug and disease networksLei Chen 0Kaiyu Chen1Bo Zhou 21. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China1. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China2. Shanghai University of Medicine & Health Sciences, Shanghai 201318, ChinaDrugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.https://www.aimspress.com/article/doi/10.3934/mbe.2023632?viewType=HTMLdrug-disease associationdrug repositioningnegative sample selectionnetwork embeddingbinary classificationrandom forest |
| spellingShingle | Lei Chen Kaiyu Chen Bo Zhou Inferring drug-disease associations by a deep analysis on drug and disease networks Mathematical Biosciences and Engineering drug-disease association drug repositioning negative sample selection network embedding binary classification random forest |
| title | Inferring drug-disease associations by a deep analysis on drug and disease networks |
| title_full | Inferring drug-disease associations by a deep analysis on drug and disease networks |
| title_fullStr | Inferring drug-disease associations by a deep analysis on drug and disease networks |
| title_full_unstemmed | Inferring drug-disease associations by a deep analysis on drug and disease networks |
| title_short | Inferring drug-disease associations by a deep analysis on drug and disease networks |
| title_sort | inferring drug disease associations by a deep analysis on drug and disease networks |
| topic | drug-disease association drug repositioning negative sample selection network embedding binary classification random forest |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023632?viewType=HTML |
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