DDA-SKF: Predicting Drug–Disease Associations Using Similarity Kernel Fusion

Drug repositioning provides a promising and efficient strategy to discover potential associations between drugs and diseases. Many systematic computational drug-repositioning methods have been introduced, which are based on various similarities of drugs and diseases. In this work, we proposed a new computational model, DDA-SKF (drug–disease associations prediction using similarity kernels fusion), which can predict novel drug indications by utilizing similarity kernel fusion (SKF) and Laplacian regularized least squares (LapRLS) algorithms. DDA-SKF integrated multiple similarities of drugs and diseases. The prediction performances of DDA-SKF are better, or at least comparable, to all state-of-the-art methods. The DDA-SKF can work without sufficient similarity information between drug indications. This allows us to predict new purpose for orphan drugs. The source code and benchmarking datasets are deposited in a GitHub repository (https://github.com/GCQ2119216031/DDA-SKF).

Download Full-text

A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs

BMC Bioinformatics ◽

10.1186/s12859-020-03906-7 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Yubin Xiao ◽

Zheng Xiao ◽

Xiang Feng ◽

Zhiping Chen ◽

Linai Kuang ◽

...

Keyword(s):

Computational Model ◽

Cross Validation ◽

State Of The Art ◽

Prediction Methods ◽

Good Prediction ◽

Average Case ◽

Comparison Results ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

Download Full-text

A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

10.21203/rs.2.18937/v3 ◽

2020 ◽

Author(s):

Yubin Xiao ◽

Zheng Xiao ◽

Xiang Feng ◽

Zhiping Chen ◽

Linai Kuang ◽

...

Keyword(s):

Computational Model ◽

Cross Validation ◽

State Of The Art ◽

Prediction Methods ◽

Good Prediction ◽

Average Case ◽

Comparison Results ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.Results: In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (5-fold CV), 10-Fold Cross Validation (10-fold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in 5-fold CV, 10-fold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.Conclusion: The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

Download Full-text

Exploiting Latent Semantic Subspaces to Derive Associations for Specific Pharmaceutical Semantics

Data Science and Engineering ◽

10.1007/s41019-020-00140-2 ◽

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 .

Download Full-text

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

Biomedical Research and Therapy ◽

10.15419/bmrat.v4is.281 ◽

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

Download Full-text

A Novel Neighborhood-Based Computational Model for Potential MiRNA-Disease Association Prediction

Computational and Mathematical Methods in Medicine ◽

10.1155/2019/5145646 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Yang Liu ◽

Xueyong Li ◽

Xiang Feng ◽

Lei Wang

Keyword(s):

Computational Model ◽

Nearest Neighbor ◽

Esophageal Neoplasms ◽

State Of The Art ◽

Biological Processes ◽

Recommendation Algorithm ◽

Diagnosis And Prognosis ◽

Disease Associations ◽

Independent Case ◽

Previous State

In recent years, more and more studies have shown that miRNAs can affect a variety of biological processes. It is important for disease prevention, treatment, diagnosis, and prognosis to study the relationships between human diseases and miRNAs. However, traditional experimental methods are time-consuming and labour-intensive. Hence, in this paper, a novel neighborhood-based computational model called NBMDA is proposed for predicting potential miRNA-disease associations. Due to the fact that known miRNA-disease associations are very rare and many diseases (or miRNAs) are associated with only one or a few miRNAs (or diseases), in NBMDA, the K-nearest neighbor (KNN) method is utilized as a recommendation algorithm based on known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases to improve its prediction accuracy. And simulation results demonstrate that NBMDA can effectively infer miRNA-disease associations with higher accuracy compared with previous state-of-the-art methods. Moreover, independent case studies of esophageal neoplasms, breast neoplasms and colon neoplasms are further implemented, and as a result, there are 47, 48, and 48 out of the top 50 predicted miRNAs having been successfully confirmed by the previously published literatures, which also indicates that NBMDA can be utilized as a powerful tool to study the relationships between miRNAs and diseases.

Download Full-text

SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks

BMC Bioinformatics ◽

10.1186/s12859-020-03950-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tamer N. Jarada ◽

Jon G. Rokne ◽

Reda Alhajj

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Cross Validation ◽

Drug Repositioning ◽

Computational Method ◽

Machine Learning Techniques ◽

Similarity Network ◽

Novel Drug ◽

Similarity Information ◽

Fold Cross Validation

Abstract Background Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. Results In this study, a novel framework SNF-NN based on deep learning is presented, where novel drug-disease interactions are predicted using drug-related similarity information, disease-related similarity information, and known drug-disease interactions. Heterogeneous similarity information related to drugs and disease is fed to the proposed framework in order to predict novel drug-disease interactions. SNF-NN uses similarity selection, similarity network fusion, and a highly tuned novel neural network model to predict new drug-disease interactions. The robustness of SNF-NN is evaluated by comparing its performance with nine baseline machine learning methods. The proposed framework outperforms all baseline methods ($$AUC-ROC$$ A U C - R O C = 0.867, and $$AUC-PR$$ A U C - P R =0.876) using stratified 10-fold cross-validation. To further demonstrate the reliability and robustness of SNF-NN, two datasets are used to fairly validate the proposed framework’s performance against seven recent state-of-the-art methods for drug-disease interaction prediction. SNF-NN achieves remarkable performance in stratified 10-fold cross-validation with $$AUC-ROC$$ A U C - R O C ranging from 0.879 to 0.931 and $$AUC-PR$$ A U C - P R from 0.856 to 0.903. Moreover, the efficiency of SNF-NN is verified by validating predicted unknown drug-disease interactions against clinical trials and published studies. Conclusion In conclusion, computational drug repositioning research can significantly benefit from integrating similarity measures in heterogeneous networks and deep learning models for predicting novel drug-disease interactions. The data and implementation of SNF-NN are available at http://pages.cpsc.ucalgary.ca/ tnjarada/snf-nn.php.

Download Full-text

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

Bioinformatics ◽

10.1093/bioinformatics/btaa062 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2839-2847 ◽

Cited By ~ 1

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.

Download Full-text

SNF-NN: Computational Method To Predict Drug-Disease Interactions Using Similarity Network Fusion and Neural Networks

10.21203/rs.3.rs-56433/v1 ◽

2020 ◽

Author(s):

Tamer Jarada ◽

Jon Rokne ◽

Reda Alhajj

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Cross Validation ◽

Drug Repositioning ◽

Computational Method ◽

Machine Learning Techniques ◽

Similarity Network ◽

Novel Drug ◽

Similarity Information ◽

Fold Cross Validation

Abstract Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. In this study, a novel framework SNF-NN based on deep learning is presented, where novel drugdisease interactions are predicted using drug-related similarity information, disease-related similarity information, and known drug-disease interactions. Heterogeneous similarity information related to drugs and disease is fed to the proposed framework in order to predict novel drug-disease interactions. SNF-NN uses similarity selection, similarity network fusion, and a highly tuned novel neural network model to predict new drug-disease interactions. The robustness of SNF-NN is evaluated by comparing its performance with nine baseline machine learning methods. The proposed framework outperforms all baseline methods (AUC − ROC = 0.867, and AUC − P R=0.876) using stratified 10-fold cross-validation. To further demonstrate the reliability and robustness of SNF-NN, two datasets are used to fairly validate the proposed framework’s performance against seven recent state-of-the-art methods for drug-disease interaction prediction. SNF-NN achieves remarkable performance in stratified 10-fold cross-validation with AUC − ROC ranging from 0.879 to 0.931 and AUC − P R from 0.856 to 0.903. Moreover, the efficiency of SNF-NN by is verified by validating predicted unknown drug-disease interactions against clinical trials and published studies. In conclusion, computational drug repositioning research can significantly benefit from integrating similarity measures in heterogeneous networks and deep learning models for predicting novel drug-disease interactions

Download Full-text

A Novel Computational Model for Predicting Potential LncRNA-Disease Associations based on Both Direct and Indirect Features of LncRNA-Disease Pairs

10.21203/rs.2.18937/v2 ◽

2020 ◽

Author(s):

Yubin Xiao ◽

Zheng Xiao ◽

Xiang Feng ◽

Zhiping Chen ◽

Linai Kuang ◽

...

Keyword(s):

Computational Model ◽

Cross Validation ◽

State Of The Art ◽

Prediction Methods ◽

Good Prediction ◽

Average Case ◽

Comparison Results ◽

Disease Associations ◽

Fold Cross Validation

Abstract Background: Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is helpful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.Results: In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (5-fold CV), 10-Fold Cross Validation (10-fold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in 5-fold CV, 10-fold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Moreover, comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.Conclusion: The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

Download Full-text

Chitosan Nanoparticles: An Approbative System for the Delivery of Herbal Bioactives

The Natural Products Journal ◽

10.2174/2210315510999201124155627 ◽

2020 ◽

Vol 10 ◽

Author(s):

Sapna Saini ◽

Sanju Nanda ◽

Anju Dhiman

Keyword(s):

Drug Delivery ◽

Drug Delivery Systems ◽

State Of The Art ◽

Chitosan Nanoparticles ◽

Delivery Systems ◽

Intrinsic Properties ◽

Amino Groups ◽

Suitable Candidate ◽

Film Forming ◽

Novel Drug

: Chitosan, a natural biodegradable polymer obtained from deacetylation of chitin, has been used as an approbative macromolecule for the development of various novel drug delivery systems. It is one of the most favorable biodegradable carriers for nanoparticulate drug delivery due to its intrinsic properties, such as biocompatibility, biodegradability, non-toxicity, availability of free reactive amino groups, and ease of chemical modification into different active derivatives. Furthermore, interesting physical properties (film-forming, gelling and thickening) make it a suitable candidate for formulations, such as films, microcapsules, beads, nanoparticles, nanofibres, nanogel and so on. Researchers have reported that chitosan nanoparticles act as a promising vehicle for herbal actives as they provide a superior alternative to traditional carriers and improve pharmaceutical efficiency. As no review of chitosan nanoparticles encapsulating herbal extracts and bioactives has been published till date, a maiden effort has been made to collate and review the use of chitosan nanoparticles for the entrapment of phytoconstituents to yield stable, efficient and safe drug delivery systems. Additionally, the paper presents a comprehensive account of the state-of the-art in fabricating herbal chitosan nanoparticles and their current pharmacological status. A list of patents on chitosan nanoparticles of herbal actives has also been included. This review is intended to serve as a didactic discourse for the formulation scientists endeavoring to develop advanced delivery systems for herbal actives.

Download Full-text