Prediction of Drug‐Drug Interactions Based on Multi‐layer Feature Selection and Data Balance

Yue, Kejuan; Zou, Beiji; Wang, Lei; Li, Xiao; Zeng, Min; Wei, Faran

doi:10.1049/cje.2017.04.005

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…6) Manually annotated targets and drugs online resource (MATADOR) is a resource for protein-chemical interactions. The manually annotated list of direct (binding) and indirect interactions between proteins and chemicals was assembled by automated textmining followed by manual curation [26,27] .…”

Section: Methodsmentioning

confidence: 99%

BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

Bai

Yan

Chen³

et al. 2019

Chinese Journal of Electronics

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Section: Methodsmentioning

confidence: 99%

BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

Bai

Yan

Chen³

et al. 2019

Chinese Journal of Electronics

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“…The ADE could be either from the prescription of a single drug or a particular class of DDIs [41]. Among patients with kidney diseases, polypharmacy is common to cause DDIs, which may cause unexpected adverse effects and even death [42]. Some ADEs are predictable and preventable, such as known DDIs and DDXs [43].…”

Section: Background and Related Workmentioning

confidence: 99%

Detection of Drug–Drug and Drug–Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests

Kang

Huang

2021

SN COMPUT. SCI.

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Drug-drug interactions (DDIs) and drug-disease interactions (DDXs) are critical issues for the healthcare system and clinical physicians. Typical statistical approaches, such as generalized linear models, cannot systematically handle the complexity of DDIs and DDXs. Although deep neural networks can predict DDIs and DDXs with high accuracy, they often require large numbers of training data, and how such black-box models arrive at predictions is still not well understood. Therefore, we propose a novel interpretable representation learning algorithm, Deep Rule Forest (DRF) to help discover rules from multi-layer tree-based models as the combinations of drug usages and disease indications to help identify DDIs and DDXs. In this paper, we introduce a real-world application of our approach to acute kidney injury (AKI), which is a typical unfavorable outcome that could be triggered by DDIs/DDXs. The sample data were obtained from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database. The experimental result shows that the combinations of several diseases and drug prescriptions are associated with AKI. Also, the DRF combined with other machine learning algorithms perform comparatively higher than typical tree/rule-based and other state-of-the-art algorithms in terms of accuracy of prediction and model interpretability.

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“…Yue et al addressed the issues of selecting discriminative features and data imbalancing in DDI detection. To increase the accuracy of DDIs detection, a multi‐layer feature selection method and an over‐sampling model are utilised [20]. Deepika et al proposed a semi‐supervised learning framework for DDI.…”

Section: Related Workmentioning

confidence: 99%