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 It should be noted that these associations are still not well established due to many of marketed drugs have been withdrawn and this could affect to outcome of the methods.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

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 SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Cheng Yan ◽  
Jianxin Wang ◽  
Wei Lan ◽  
Fang-Xiang Wu ◽  
Yi Pan
Keyword(s):  
Computational Methods ◽  
Drug Target ◽  
Large Scale ◽  
High Efficiency ◽  
Drug Repositioning ◽  
External Validation ◽  
Complex Diseases ◽  
Biological Databases ◽  
Biomolecular Networks ◽  
New Chemical Entities

It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

Exploiting Latent Semantic Subspaces to Derive Associations for Specific Pharmaceutical Semantics

2020 ◽  
Vol 5 (4) ◽  
pp. 333-345
Author(s):  
Janus Wawrzinek ◽  
José María González Pinto ◽  
Oliver Wiehr ◽  
Wolf-Tilo Balke
Keyword(s):  
Domain Knowledge ◽  
State Of The Art ◽  
Drug Repositioning ◽  
Hypothesis Generation ◽  
Semantic Relations ◽  
Automatic Extraction ◽  
Biomedical Domain ◽  
Important Research ◽  
Disease Associations ◽  
Active Substances

Abstract State-of-the-art approaches in the field of neural embedding models (NEMs) enable progress in the automatic extraction and prediction of semantic relations between important entities like active substances, diseases, and genes. In particular, the prediction property is making them valuable for important research-related tasks such as hypothesis generation and drug repositioning. A core challenge in the biomedical domain is to have interpretable semantics from NEMs that can distinguish, for instance, between the following two situations: (a) drug x induces disease y and (b) drug x treats disease y. However, NEMs alone cannot distinguish between associations such as treats or induces. Is it possible to develop a model to learn a latent representation from the NEMs capable of such disambiguation? To what extent do we need domain knowledge to succeed in the task? In this paper, we answer both questions and show that our proposed approach not only succeeds in the disambiguation task but also advances current growing research efforts to find real predictions using a sophisticated retrospective analysis. Furthermore, we investigate which type of associations is generally better contextualized and therefore probably has a stronger influence in our disambiguation task. In this context, we present an approach to extract an interpretable latent semantic subspace from the original embedding space in which therapeutic drug–disease associations are more likely .

scholarly journals Assessing drug target suitability using TargetMine

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 233
Author(s):  
Yi-An Chen ◽  
Erika Yogo ◽  
Naoko Kurihara ◽  
Tomoshige Ohno ◽  
Chihiro Higuchi ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Drug Target ◽  
Drug Targets ◽  
Protein Structures ◽  
Pharmaceutical Research ◽  
Drug Efficacy ◽  
Chemical Compounds ◽  
Information Gathering ◽  
Data Sources ◽  
Disease Associations

In selecting drug target candidates for pharmaceutical research, the linkage to disease and the tractability of the target are two important factors that can ultimately determine the drug efficacy. Several existing resources can provide gene-disease associations, but determining whether such a list of genes are attractive drug targets often requires further information gathering and analysis. In addition, few resources provide the information required to evaluate the tractability of a target. To address these issues, we have updated TargetMine, a data warehouse for assisting target prioritization, by integrating new data sources for gene-disease associations and enhancing functionalities for target assessment. As a data mining platform that integrates a variety of data sources, including protein structures and chemical compounds, TargetMine now offers a powerful and flexible interface for constructing queries to check genetic evidence, tractability and other relevant features for the candidate genes. We demonstrate these features by using several specific examples.

A systems-level analysis of drug–target–disease associations for drug repositioning

2017 ◽  
Vol 17 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Kayleigh D Rutherford ◽  
Gaston K Mazandu ◽  
Nicola J Mulder
Keyword(s):  
Drug Target ◽  
Drug Repositioning ◽  
Disease Associations ◽  
Target Disease ◽  
Level Analysis

Drug repositioning through integration of prior knowledge and projections of drugs and diseases

2019 ◽  
Vol 35 (20) ◽  
pp. 4108-4119 ◽  
Author(s):  
Ping Xuan ◽  
Yangkun Cao ◽  
Tiangang Zhang ◽  
Xiao Wang ◽  
Shuxiang Pan ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Drug Repositioning ◽  
Heterogeneous Data ◽  
Supplementary Information ◽  
Therapeutic Effects ◽  
Potential Candidate ◽  
Multiple Sources ◽  
Chemical Structures ◽  
Disease Associations ◽  
Drug Similarity

Abstract 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.

scholarly journals Uncovering new drug properties in target-based drug-drug similarity networks

2020 ◽  
Author(s):  
Lucreţia Udrescu ◽  
Paul Bogdan ◽  
Aimée Chiş ◽  
Ioan Ovidiu Sîrbu ◽  
Alexandru Topîrceanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Azelaic Acid ◽  
Drug Target ◽  
Drug Repositioning ◽  
Traditional Approach ◽  
Test Procedure ◽  
Drug Repurposing ◽  
Biological Targets ◽  
Chemical Structures ◽  
Drug Similarity

ABSTRACTDespite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach – based on knowledge about the chemical structures – cannot fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug-target interactions to infer drug properties. To this end, we define drug similarity based on drug-target interactions and build a weighted Drug-Drug Similarity Network according to the drug-drug similarity relationships. Using an energy-model network layout, we generate drug communities that are associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them as drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. By using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure, based on molecular docking, to further analyze the repurposing of Azelaic acid and Meprobamate.

scholarly journals Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Saee Paliwal ◽  
Alex de Giorgio ◽  
Daniel Neil ◽  
Jean-Baptiste Michel ◽  
Alix MB Lacoste
Keyword(s):  
Drug Target ◽  
Disease Gene ◽  
Historical Data ◽  
State Of The Art ◽  
Target Identification ◽  
Tensor Factorization ◽  
Prioritization Method ◽  
Drug Target Identification ◽  
Heterogeneous Knowledge

Abstract Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures1–3. Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA.

DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion

2020 ◽  
Vol 36 (9) ◽  
pp. 2839-2847 ◽  
Author(s):  
Wenjuan Zhang ◽  
Hunan Xu ◽  
Xiaozhong Li ◽  
Qiang Gao ◽  
Lin Wang
Keyword(s):  
Drug Repositioning ◽  
Matrix Completion ◽  
Disease Association ◽  
Data Sources ◽  
Supplementary Information ◽  
Similarity Matrix ◽  
Latent Factors ◽  
Discovery Research ◽  
Disease Associations ◽  
Novel Drug

Abstract Motivation One of the most important problems in drug discovery research is to precisely predict a new indication for an existing drug, i.e. drug repositioning. Recent recommendation system-based methods have tackled this problem using matrix completion models. The models identify latent factors contributing to known drug-disease associations, and then infer novel drug-disease associations by the correlations between latent factors. However, these models have not fully considered the various drug data sources and the sparsity of the drug-disease association matrix. In addition, using the global structure of the drug-disease association data may introduce noise, and consequently limit the prediction power. Results In this work, we propose a novel drug repositioning approach by using Bayesian inductive matrix completion (DRIMC). First, we embed four drug data sources into a drug similarity matrix and two disease data sources in a disease similarity matrix. Then, for each drug or disease, its feature is described by similarity values between it and its nearest neighbors, and these features for drugs and diseases are mapped onto a shared latent space. We model the association probability for each drug-disease pair by inductive matrix completion, where the properties of drugs and diseases are represented by projections of drugs and diseases, respectively. As the known drug-disease associations have been manually verified, they are more trustworthy and important than the unknown pairs. We assign higher confidence levels to known association pairs compared with unknown pairs. We perform comprehensive experiments on three benchmark datasets, and DRIMC improves prediction accuracy compared with six stat-of-the-art approaches. Availability and implementation Source code and datasets are available at https://github.com/linwang1982/DRIMC. Supplementary information Supplementary data are available at Bioinformatics online.

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