LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning

Liang, Xujun; Zhang, Peng Fei; Lu, Yan; Fu, Ying; Fang, Peng; Qu, Lingzhi; Shao, Meiying; Chen, Yongheng; Chen, Zhuchu

doi:10.1093/bioinformatics/btw770

Cited by 110 publications

(93 citation statements)

References 30 publications

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“…Then, a similarity-based information diffusion method was used to estimate the probabilities of unknown drug-disease associations. LRSSL (Liang et al, 2017) modeled the prediction of drug indications as a joint optimization problem by combining Laplacian regularization with a sparse learning framework, and then an iteratively updating algorithm was implemented to obtain a locally optimal solution. DRRS (Luo et al, 2018) stated drug repositioning as a recommendation problem and utilized a matrix completion algorithm on a block matrix which was concatenated by a drug-disease association matrix, a drug-drug similarity matrix, and a disease-disease similarity matrix.…”

Section: Comparison With State-of-the-art Association Prediction Methodsmentioning

confidence: 99%

“…Moghadam et al (2016) adopted the kernel fusion technique to combine different drug features and disease features, and then built SVM models. Liang et al (2017) proposed a Laplacian regularized sparse subspace learning method (LRSSL) which integrated drug chemical structures, drug target domains, and target ontology. Zhang et al (2016b) defined this task as the recommender problem, and introduced restricted Boltzmann machine and collaborative filtering to predict unobserved side effects.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

Huang

Yang

et al. 2020

Front. Bioeng. Biotechnol.

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Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease associations involve different association types, such as drug indications and drug side effects. However, the existing models for predicting drug-disease associations merely concentrate on independent tasks: recommending novel indications to benefit drug repositioning, predicting potential side effects to prevent drug-induced risk, or only determining the existence of drug-disease association. They ignore crucial prior knowledge of the correlations between different association types. Since the Comparative Toxicogenomics Database (CTD) annotates the drug-disease associations as therapeutic or marker/mechanism, we consider predicting the two types of association. To this end, we propose a collective matrix factorization-based multi-task learning method (CMFMTL) in this paper. CMFMTL handles the problem as multi-task learning where each task is to predict one type of association, and two tasks complement and improve each other by capturing the relatedness between them. First, drug-disease associations are represented as a bipartite network with two types of links representing therapeutic effects and non-therapeutic effects. Then, CMFMTL, respectively, approximates the association matrix regarding each link type by matrix tri-factorization, and shares the low-dimensional latent representations for drugs and diseases in the two related tasks for the goal of collective learning. Finally, CMFMTL puts the two tasks into a unified framework and an efficient algorithm is developed to solve our proposed optimization problem. In the computational experiments, CMFMTL outperforms several state-of-the-art methods both in the two tasks. Moreover, case studies show that CMFMTL helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their association types.

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Section: Comparison With State-of-the-art Association Prediction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

Huang

Yang

et al. 2020

Front. Bioeng. Biotechnol.

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“…The first benchmark dataset, termed SND, was assembled and used along with two datasets, i.e. Cdataset [16] and LRSSL [27], which were used in previous studies [16], [22], [25]- [27].…”

Section: Methodsmentioning

confidence: 99%

“…LRSSL benchmark dataset was obtained from the paper [27]. It consists of three different types of information, namely drug-disease interaction data, drug-related similarity data, and disease-related similarity data.…”

Section: Lrssl Benchmark Datasetmentioning

confidence: 99%

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

Jarada

Rokne

Alhajj

2020

Preprint

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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

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“…For example, Lu et al used regularized nuclear classifiers to construct drug and disease predictions 1 . Liang et al used a Laplacian regularization algorithm for sparse subspaces to construct a drug repositioning prediction model: LRSSL2 2 . The method incorporates information such as medicinal chemistry information and drug targets.…”

mentioning

confidence: 99%

SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network

Jiang

Huang

You

2020

Sci Rep

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Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-disease associations identified by high-throughput technologies is increasing, the experimental methods are time consuming and expensive. As supplement to them, many computational methods have been developed for an accurate in silico prediction for new drug-disease associations. In this work, we present a novel computational model combining sparse auto-encoder and rotation forest (SAeRof) to predict drug-disease association. Gaussian interaction profile kernel similarity, drug structure similarity and disease semantic similarity were extracted for exploring the association among drugs and diseases. On this basis, a rotation forest classifier based on sparse auto-encoder is proposed to predict the association between drugs and diseases. In order to evaluate the performance of the proposed model, we used it to implement 10-fold cross validation on two golden standard datasets, Fdataset and Cdataset. As a result, the proposed model achieved AUCs (Area Under the ROC Curve) of Fdataset and Cdataset are 0.9092 and 0.9323, respectively. For performance evaluation, we compared SAEROF with the state-of-the-art support vector machine (SVM) classifier and some existing computational models. Three human diseases (Obesity, Stomach Neoplasms and Lung Neoplasms) were explored in case studies. As a result, more than half of the top 20 drugs predicted were successfully confirmed by the Comparative Toxicogenomics Database(CTD database). This model is a feasible and effective method to predict drug-disease correlation, and its performance is significantly improved compared with existing methods.

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LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 110 publications

References 30 publications

Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

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

SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network

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