Predicting potential side effects of drugs by recommender methods and ensemble learning

Zhang, Wen; Zou, Hua; Luo, Laima; Liu, Qianchao; Wu, Wei; Xiao, Wenyi

doi:10.1016/j.neucom.2015.08.054

Cited by 122 publications

(75 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This criterion can be exchanged for any health-related criterion. For example, by using medication data and further patient data, recommender systems could be used to suggest medication that has less side effects (Zhang et al, 2016). Health recommender systems could suggest therapies that better match patients' dispositions and adherence behaviors (Hidalgo et al, 2014;Esteban et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Valdez

Ziefle

2019

International Journal of Human-Computer Studies

View full text Add to dashboard Cite

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n = 521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.

show abstract

Section: Introductionmentioning

confidence: 99%

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Valdez

Ziefle

2019

International Journal of Human-Computer Studies

View full text Add to dashboard Cite

show abstract

“…Ensemble learning is a useful technique that aggregates multiple machine learning models to achieve overall high prediction accuracy as well as good generalization [39]. Ensemble learning has been applied to a great number of applications in bioinformatics [29, 40, 41]. …”

Section: Methodsmentioning

confidence: 99%

“…Multi-source data provide biological information, chemical information, phenotypic information and known interactions to characterize drug-drug interactions. To make use of diverse information, we adopt three representative methods, i.e., the neighbor recommender method [28, 29], the random walk method and the matrix perturbation method [30], to build different prediction models. According to performances of prediction models, we evaluate the usefulness of different information sources for the DDI prediction.…”

Section: Introductionmentioning

confidence: 99%

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

et al. 2017

Self Cite

View full text Add to dashboard Cite

BackgroundDrug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance.ResultsSince many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances.ConclusionsThe experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https://github.com/zw9977129/drug-drug-interaction/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1415-9) contains supplementary material, which is available to authorized users.

show abstract

“…We further investigated the improvement on macroaveraging values of the three performance measurement indices (See Table 2). They were calculated as the average values of all side-effects using equation (14). The highest performance of each classifier was highlighted via bold numbers.…”

Section: Performance Improvement Brought By the Selection Of Highly-rmentioning

confidence: 99%

“…However, such experimental predictions are expensive, timeconsuming, and tedious. Recently, several computational methods have been proposed to tackle the side-effect prediction problem based on drug profiles [2,[5][6][7][8][9][10][11][12][13][14][15]. These methods can be categorized into target protein-based methods and chemical structure-based methods.…”

Section: Introductionmentioning

confidence: 99%

Inverse similarity and reliable negative samples for drug side-effect prediction

et al. 2019

View full text Add to dashboard Cite

Background: In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement. Methods: Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions. Results: The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, achieving much better performance than those based on a single type of drug property. Besides, the four machine-learning algorithms achieved significant improvement in macro-averaging F1-score (e.g., SVM from 0.655 to 0.898), macro-averaging precision (e.g., RBF from 0.592 to 0.828) and macro-averaging recall (e.g., KNN from 0.651 to 0.772) complimentarily attributed to the highly-reliable negative samples selected by the proposed method. Conclusions:The results suggest that the inverse similarity hypothesis and the integration of different drug properties are valuable for side-effect prediction. The selection of highly-reliable negative samples can also make significant contributions to the performance improvement.

show abstract

Predicting potential side effects of drugs by recommender methods and ensemble learning

Cited by 122 publications

References 33 publications

The users’ perspective on the privacy-utility trade-offs in health recommender systems

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data

Inverse similarity and reliable negative samples for drug side-effect prediction

Contact Info

Product

Resources

About