scholarly journals A Drug Repositioning Algorithm Based on Deep Auto-Encoder and Adaptive Fusion

2020 ◽  
Author(s):  
Peng Chen ◽  
Tianjiazhi Bao ◽  
Xiaosheng Yu ◽  
Zhongtu Liu
Keyword(s):  
Drug Target ◽  
Chemical Structure ◽  
Drug Repositioning ◽  
Disease Association ◽  
Target Protein ◽  
New Drugs ◽  
Data Set ◽  
Fusion Methods ◽  
Disease Associations ◽  
Adaptive Fusion

Abstract Background: Drug repositioning has aroused extensive attention by scholars at home and abroad due to its effective reduction in development cost and time of new drugs. However, the current drug repositioning based on computational analysis methods is still limited by the problems of data sparse and fusion methods, so we use autoencoders and adaptive fusion methods to calculate drug repositioning.Results: In this paper, a drug repositioning algorithm based on deep auto-encoder and adaptive fusion has been proposed against the problems of declined precision and low-efficiency multi-source data fusion caused by data sparseness. Specifically, the drug is repositioned through fusing drug-disease association, drug target protein, drug chemical structure and drug side effects. To begin with, drug feature data integrated by drug target protein and chemical structure were processed with dimension reduction via a deep auto-encoder, to obtain feature representation more densely and abstractly. On this basis, disease similarity was computed by the drug-disease association data, while drug similarity was calculated by drug feature and drug-side effect data. Besides, the predictions of drug-disease associations were calculated using a Top-k neighbor method that is more suitable for drug repositioning. Finally, a predicted matrix for drug-disease associations has been acquired upon fusing a wide variety of data via adaptive fusion. According to the experimental results, the proposed algorithm has higher precision and recall rate in comparison to DRCFFS, SLAMS and BADR algorithms that use the same data set for computation.Conclusion: our proposed algorithm contributes to studying novel uses of drugs, as can be seen from the case analysis of Alzheimer's disease. Therefore, it can provide a certain auxiliary effect for clinical trials of drug repositioning

scholarly journals A drug repositioning algorithm based on a deep autoencoder and adaptive fusion

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Peng Chen ◽  
Tianjiazhi Bao ◽  
Xiaosheng Yu ◽  
Zhongtu Liu
Keyword(s):  
Drug Target ◽  
Drug Repositioning ◽  
Recall Rate ◽  
New Drugs ◽  
Target Proteins ◽  
Chemical Structures ◽  
Fusion Methods ◽  
Disease Associations ◽  
Adaptive Fusion ◽  
Low Efficiency

Abstract Background Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods; thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning. Results In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset. Conclusion The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.

scholarly journals Predicting Drug-Disease Association Based on Ensemble Strategy

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.

Exploring Disease Association from the NHANES Data

2011 ◽  
pp. 157-173
Author(s):  
Zhengzheng Xing ◽  
Jian Pei
Keyword(s):  
Pattern Mining ◽  
Disease Association ◽  
Medical Data ◽  
Mining Method ◽  
Medical Data Mining ◽  
Data Set ◽  
Hierarchical Tree ◽  
Disease Associations ◽  
Disease Pattern ◽  
Influence Graph

Finding associations among different diseases is an important task in medical data mining. The NHANES data is a valuable source in exploring disease associations. However, existing studies analyzing the NHANES data focus on using statistical techniques to test a small number of hypotheses. This NHANES data has not been systematically explored for mining disease association patterns. In this regard, this paper proposes a direct disease pattern mining method and an interactive disease pattern mining method to explore the NHANES data. The results on the latest NHANES data demonstrate that these methods can mine meaningful disease associations consistent with the existing knowledge and literatures. Furthermore, this study provides summarization of the data set via a disease influence graph and a disease hierarchical tree.

scholarly journals PROMISCUOUS 2.0: a resource for drug-repositioning

2020 ◽  
Vol 49 (D1) ◽  
pp. D1373-D1380
Author(s):  
Kathleen Gallo ◽  
Andrean Goede ◽  
Andreas Eckert ◽  
Barbara Moahamed ◽  
Robert Preissner ◽  
...  
Keyword(s):  
Side Effects ◽  
Small Molecules ◽  
User Experience ◽  
Drug Target ◽  
Drug Repositioning ◽  
Dynamic Network ◽  
Structural Similarity ◽  
New Drugs ◽  
Entry Level ◽  
Development Costs

Abstract The development of new drugs for diseases is a time-consuming, costly and risky process. In recent years, many drugs could be approved for other indications. This repurposing process allows to effectively reduce development costs, time and, ultimately, save patients’ lives. During the ongoing COVID-19 pandemic, drug repositioning has gained widespread attention as a fast opportunity to find potential treatments against the newly emerging disease. In order to expand this field to researchers with varying levels of experience, we made an effort to open it to all users (meaning novices as well as experts in cheminformatics) by significantly improving the entry-level user experience. The browsing functionality can be used as a global entry point to collect further information with regards to small molecules (∼1 million), side-effects (∼110 000) or drug-target interactions (∼3 million). The drug-repositioning tab for small molecules will also suggest possible drug-repositioning opportunities to the user by using structural similarity measurements for small molecules using two different approaches. Additionally, using information from the Promiscuous 2.0 Database, lists of candidate drugs for given indications were precomputed, including a section dedicated to potential treatments for COVID-19. All the information is interconnected by a dynamic network-based visualization to identify new indications for available compounds. Promiscuous 2.0 is unique in its functionality and is publicly available at http://bioinformatics.charite.de/promiscuous2.

Computational drug repositioning based on multi-similarities bilinear matrix factorization

2020 ◽  
Author(s):  
Mengyun Yang ◽  
Gaoyan Wu ◽  
Qichang Zhao ◽  
Yaohang Li ◽  
Jianxin Wang
Keyword(s):  
Matrix Factorization ◽  
Drug Repositioning ◽  
Disease Association ◽  
Biological Entity ◽  
Biomedical Data ◽  
Supplementary Data ◽  
Practical Applications ◽  
Disease Associations ◽  
Association Matrix ◽  
Similarity Matrices

Abstract With the development of high-throughput technology and the accumulation of biomedical data, the prior information of biological entity can be calculated from different aspects. Specifically, drug–drug similarities can be measured from target profiles, drug–drug interaction and side effects. Similarly, different methods and data sources to calculate disease ontology can result in multiple measures of pairwise disease similarities. Therefore, in computational drug repositioning, developing a dynamic method to optimize the fusion process of multiple similarities is a crucial and challenging task. In this study, we propose a multi-similarities bilinear matrix factorization (MSBMF) method to predict promising drug-associated indications for existing and novel drugs. Instead of fusing multiple similarities into a single similarity matrix, we concatenate these similarity matrices of drug and disease, respectively. Applying matrix factorization methods, we decompose the drug–disease association matrix into a drug-feature matrix and a disease-feature matrix. At the same time, using these feature matrices as basis, we extract effective latent features representing the drug and disease similarity matrices to infer missing drug–disease associations. Moreover, these two factored matrices are constrained by non-negative factorization to ensure that the completed drug–disease association matrix is biologically interpretable. In addition, we numerically solve the MSBMF model by an efficient alternating direction method of multipliers algorithm. The computational experiment results show that MSBMF obtains higher prediction accuracy than the state-of-the-art drug repositioning methods in cross-validation experiments. Case studies also demonstrate the effectiveness of our proposed method in practical applications. Availability: The data and code of MSBMF are freely available at https://github.com/BioinformaticsCSU/MSBMF. Corresponding author: Jianxin Wang, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. E-mail: [email protected] Supplementary Data: Supplementary data are available online at https://academic.oup.com/bib.

scholarly journals The Performance of Gene Expression Signature-Guided Drug–Disease Association in Different Categories of Drugs and Diseases

Molecules ◽  
2020 ◽  
Vol 25 (12) ◽  
pp. 2776
Author(s):  
Xiguang Qi ◽  
Mingzhe Shen ◽  
Peihao Fan ◽  
Xiaojiang Guo ◽  
Tianqi Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Drug Repositioning ◽  
Gene Expression Signature ◽  
Disease Association ◽  
Mtor Signaling Pathway ◽  
Immune System Diseases ◽  
Expression Signature ◽  
Chemotherapy Drugs ◽  
Disease Associations

A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs–disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug–disease associations through the analysis of the similarity of GES profiles on known pairs of drug–disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug–disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway.

scholarly journals Hybrid attentional memory network for computational drug repositioning

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jieyue He ◽  
Xinxing Yang ◽  
Zhuo Gong ◽  
lbrahim Zamit
Keyword(s):  
Drug Repositioning ◽  
Cold Start ◽  
New Drugs ◽  
Data Sets ◽  
Latent Factor ◽  
Disease Associations ◽  
Memory Network ◽  
Predicted Values ◽  
New Perspective ◽  
Cold Start Problem

Abstract Background Drug repositioning has been an important and efficient method for discovering new uses of known drugs. Researchers have been limited to one certain type of collaborative filtering (CF) models for drug repositioning, like the neighborhood based approaches which are good at mining the local information contained in few strong drug–disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug–disease associations. Few researchers have combined these two types of CF models to derive a hybrid model which can offer the advantages of both. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. Results Inspired by the memory network, we propose the hybrid attentional memory network (HAMN) model, a deep architecture combining two classes of CF models in a nonlinear manner. First, the memory unit and the attention mechanism are combined to generate a neighborhood contribution representation to capture the local structure of few strong drug–disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug–disease associations. During this process, ancillary information of drugs and diseases can help alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is coupled with the drug latent factor and disease latent factor to produce predicted values. Comprehensive experimental results on two data sets demonstrate that our proposed HAMN model outperforms other comparison models based on the AUC, AUPR and HR indicators. Conclusions Through the performance on two drug repositioning data sets, we believe that the HAMN model proposes a new solution to improve the prediction accuracy of drug–disease associations and give pharmaceutical personnel a new perspective to develop new drugs.

scholarly journals A miRNA-Driven Inference Model to Construct Potential Drug-Disease Associations for Drug Repositioning

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Hailin Chen ◽  
Zuping Zhang
Keyword(s):  
Case Studies ◽  
Drug Repositioning ◽  
Disease Association ◽  
Potential Drug ◽  
Inference Model ◽  
Excellent Performance ◽  
Model Case ◽  
Disease Associations ◽  
New Perspective ◽  
Inappropriate Expression

Increasing evidence discovered that the inappropriate expression of microRNAs (miRNAs) will lead to many kinds of complex diseases and drugs can regulate the expression level of miRNAs. Therefore human diseases may be treated by targeting some specific miRNAs with drugs, which provides a new perspective for drug repositioning. However, few studies have attempted to computationally predict associations between drugs and diseases via miRNAs for drug repositioning. In this paper, we developed an inference model to achieve this aim by combining experimentally supported drug-miRNA associations and miRNA-disease associations with the assumption that drugs will form associations with diseases when they share some significant miRNA partners. Experimental results showed excellent performance of our model. Case studies demonstrated that some of the strongly predicted drug-disease associations can be confirmed by the publicly accessible database CTD (www.ctdbase.org), which indicated the usefulness of our inference model. Moreover, candidate miRNAs as molecular hypotheses underpinning the associations were listed to guide future experiments. The predicted results were released for further studies. We expect that this study will provide help in our understanding of drug-disease association prediction and in the roles of miRNAs in drug repositioning.

scholarly journals In silico drug repositioning using deep learning and comprehensive similarity measures

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Hai-Cheng Yi ◽  
Zhu-Hong You ◽  
Lei Wang ◽  
Xiao-Rui Su ◽  
Xi Zhou ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Similarity Measures ◽  
Predictive Ability ◽  
New Drugs ◽  
Potential Drug ◽  
Proposed Model ◽  
Disease Associations ◽  
Benchmark Datasets ◽  
Gated Recurrent Units ◽  
New Treatments

Abstract Background Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug–disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. Methods In this work, we develop a deep gated recurrent units model to predict potential drug–disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug–disease interactions. Results The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. Conclusion The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.

scholarly journals ID:2047 Drug respositioning by integrating known disease-gene and drug-target associations

2017 ◽  
Vol 4 (S) ◽  
pp. 76
Author(s):  
Duc-Hau Le ◽  
Duc-Hau Le
Keyword(s):  
Computational Methods ◽  
Drug Target ◽  
Disease Gene ◽  
State Of The Art ◽  
Drug Repositioning ◽  
Least Square ◽  
Data Sources ◽  
Chemical Structures ◽  
Drug Compounds ◽  
Disease Associations

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs, diseases and different approaches. Depending on where the discovery of drug-disease relationships comes from, proposed computational methods can be categorized as either ‘drug-based’ or ‘disease-based’. The proposed methods are usually based on an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarity between drugs and between diseases is usually used as inputs. In addition, known drug-disease associations are also needed for the methods. It should be noted that these associations are still not well established due to many of marketed drugs have been withdrawn and this could affect to outcome of the methods. In this study, instead of using the known drug-disease associations, we based on known disease-gene and drug-target associations. In addition, similarity between drugs measured by chemical structures of drug compounds and similarity between diseases sharing phenotypes are used. Then, a semi-supervised learning model, Regularized Least Square (RLS), which can exploit these information effectively, is used to find new uses of drugs. Experiment results demonstrate that our method, namely RLSDR, outperforms several state-of-the-art existing methods in terms of area under the ROC curve (AUC). Novel indications for a number of drugs are identified and validated by evidences from different resources

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