Drug and disease similarity calculation platform for drug repositioning

Abstract Background Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. Results In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. Conclusions The experiments on a gold standard dataset which contains 1933 validated drug–disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

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Practical approach for disease similarity calculation based on disease phenotype, etiology, and locational clues in disease names

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2016.7822659 ◽

2016 ◽

Cited By ~ 4

Author(s):

Mai Omura ◽

Noboru Sonehara ◽

Takashi Okumura

Keyword(s):

Practical Approach ◽

Disease Phenotype ◽

Disease Similarity ◽

Similarity Calculation

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Disease classification: from phenotypic similarity to integrative genomics and beyond

Briefings in Bioinformatics ◽

10.1093/bib/bby049 ◽

2019 ◽

Vol 20 (5) ◽

pp. 1769-1780 ◽

Cited By ~ 6

Author(s):

Mikhail G Dozmorov

Keyword(s):

Molecular Mechanisms ◽

Drug Repositioning ◽

Real Life ◽

Disease Classification ◽

Data Sets ◽

Integrative Genomics ◽

Cellular Functions ◽

Complementary Approach ◽

Disease Similarity ◽

Based Medicine

Abstract A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

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Predicting Drug-Disease Association Based on Ensemble Strategy

Frontiers in Genetics ◽

10.3389/fgene.2021.666575 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jianlin Wang ◽

Wenxiu Wang ◽

Chaokun Yan ◽

Junwei Luo ◽

Ge Zhang

Keyword(s):

Prediction Models ◽

Drug Repositioning ◽

Predictive Ability ◽

Disease Association ◽

New Drugs ◽

Similarity Network ◽

Disease Similarity ◽

Ensemble Strategy ◽

Disease Associations ◽

Reducing Costs

Drug repositioning is used to find new uses for existing drugs, effectively shortening the drug research and development cycle and reducing costs and risks. A new model of drug repositioning based on ensemble learning is proposed. This work develops a novel computational drug repositioning approach called CMAF to discover potential drug-disease associations. First, for new drugs and diseases or unknown drug-disease pairs, based on their known neighbor information, an association probability can be obtained by implementing the weighted K nearest known neighbors (WKNKN) method and improving the drug-disease association information. Then, a new drug similarity network and new disease similarity network can be constructed. Three prediction models are applied and ensembled to enable the final association of drug-disease pairs based on improved drug-disease association information and the constructed similarity network. The experimental results demonstrate that the developed approach outperforms recent state-of-the-art prediction models. Case studies further confirm the predictive ability of the proposed method. Our proposed method can effectively improve the prediction results.

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MicroPattern: a web-based tool for microbe set enrichment analysis and disease similarity calculation based on a list of microbes

Scientific Reports ◽

10.1038/srep40200 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 8

Author(s):

Wei Ma ◽

Chuanbo Huang ◽

Yuan Zhou ◽

Jianwei Li ◽

Qinghua Cui

Keyword(s):

Enrichment Analysis ◽

Web Based ◽

Disease Similarity ◽

Similarity Calculation

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A Novel Drug Repositioning Approach Based on Integrative Multiple Similarity Measures

Current Molecular Medicine ◽

10.2174/1566524019666191115103307 ◽

2020 ◽

Vol 20 (6) ◽

pp. 442-451

Author(s):

Chaokun Yan ◽

Luping Feng ◽

Wenxiu Wang ◽

Jianlin Wang ◽

Ge Zhang ◽

...

Keyword(s):

Similarity Measure ◽

Heterogeneous Network ◽

Drug Repositioning ◽

Similarity Measures ◽

Superior Performance ◽

Practical Significance ◽

Random Walk Algorithm ◽

Disease Similarity ◽

Novel Drug ◽

Walk Algorithm

Background: Drug repositioning refers to discovering new indications for the existing drugs, which can improve the efficiency of drug research and development. Methods: In this work, a novel drug repositioning approach based on integrative multiple similarity measure, called DR_IMSM, is proposed. The process of integrative similarity measure contains three steps. First, a heterogeneous network can be constructed based on known drug-disease association, shared entities information for drug pairwise and diseases pairwise. Second, a deep learning method, DeepWalk, is used to capture the topology similarity for drug and disease. Third, a similarity integration and adjusting process is further conducted to obtain more comprehensive drug and disease similarity measure, respectively. Results: On this basis, a Bi-random walk algorithm is implemented in the constructed heterogeneous network to rank diseases for each drug. Compared with other approaches, the proposed DR_IMSM can achieve superior performance in terms of AUC on the gold standard datasets. Case studies further confirm the practical significance of DR_IMSM.

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Learning from low-rank multimodal representations for predicting disease-drug associations

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01648-x ◽

2021 ◽

Vol 21 (S1) ◽

Author(s):

Pengwei Hu ◽

Yu-an Huang ◽

Jing Mei ◽

Henry Leung ◽

Zhan-heng Chen ◽

...

Keyword(s):

Domain Knowledge ◽

Computational Models ◽

Drug Repositioning ◽

Prediction Method ◽

Substantial Improvement ◽

Multimodal Fusion ◽

Low Rank ◽

Essential Information ◽

Similarity Network ◽

Disease Similarity

Abstract Background Disease-drug associations provide essential information for drug discovery and disease treatment. Many disease-drug associations remain unobserved or unknown, and trials to confirm these associations are time-consuming and expensive. To better understand and explore these valuable associations, it would be useful to develop computational methods for predicting unobserved disease-drug associations. With the advent of various datasets describing diseases and drugs, it has become more feasible to build a model describing the potential correlation between disease and drugs. Results In this work, we propose a new prediction method, called LMFDA, which works in several stages. First, it studies the drug chemical structure, disease MeSH descriptors, disease-related phenotypic terms, and drug-drug interactions. On this basis, similarity networks of different sources are constructed to enrich the representation of drugs and diseases. Based on the fused disease similarity network and drug similarity network, LMFDA calculated the association score of each pair of diseases and drugs in the database. This method achieves good performance on Fdataset and Cdataset, AUROCs were 91.6% and 92.1% respectively, higher than many of the existing computational models. Conclusions The novelty of LMFDA lies in the introduction of multimodal fusion using low-rank tensors to fuse multiple similar networks and combine matrix complement technology to predict potential association. We have demonstrated that LMFDA can display excellent network integration ability for accurate disease-drug association inferring and achieve substantial improvement over the advanced approach. Overall, experimental results on two real-world networks dataset demonstrate that LMFDA able to delivers an excellent detecting performance. Results also suggest that perfecting similar networks with as much domain knowledge as possible is a promising direction for drug repositioning.

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