Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).
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Int J Surg
January 2025
Department of Orthopedics, Civil Aviation General Hospital, Beijing, China.
Background: Dural arteriovenous fistulas (DAVFs) pose a significant health threat owing to their high misdiagnosis rate. Case reports suggest that DAVFs or related acute events may follow medication use; however, drug-related risk factors remain unclear. In clinical practice, the concomitant use of multiple drugs for therapy is known as "polypharmacy situations," further increasing the risk of drug-induced DAVF.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Division of Software, Yonsei University, Mirae Campus, Yeonsedae-gil 1, Wonju-si, 26493 Gangwon-do Korea.
Purpose: Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery. Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations. However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.
View Article and Find Full Text PDFExpert Opin Drug Saf
December 2024
Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia.
Introduction: The third-generation antiseizure medications used for the treatment of focal seizures, lacosamide, eslicarbazepine acetate, perampanel, brivaracetam, and cenobamate, may elicit serious adverse reactions which could be preventable if a prescriber is acquainted with the risk factors.
Areas Covered: The literature search was conducted in MEDLINE, SCOPUS, and EBSCO databases, without time and language restrictions. Only clinical studies, observational human studies, case reports, and case series that reported serious adverse drug reactions and risk factors were considered.
Comput Biol Chem
December 2024
Department of Computer Science and Engineering, Indian Institute of Information Technology, Nagpur, India. Electronic address:
Drug Repurposing gives us facility to find the new uses of previously developed drugs rather than developing new drugs from start. Particularly during pandemic, drug repurposing caught much attention to provide new applications of the previously approved drugs. In our research, we provide a novel method for drug repurposing based on feature learning process from drug-disease-gene network.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Information Science and Technology College, Dalian Maritime University, No.1 Linghai Road, Dalian 116026, Liaoning, China.
Drug repositioning, which involves identifying new therapeutic indications for approved drugs, is pivotal in accelerating drug discovery. Recently, to mitigate the effect of label sparsity on inferring potential drug-disease associations (DDAs), graph contrastive learning (GCL) has emerged as a promising paradigm to supplement high-quality self-supervised signals through designing auxiliary tasks, then transfer shareable knowledge to main task, i.e.
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