Varyasyonel Çizge Otokodlayıcı Kullanarak Nanoparçacık, İlaç ve Kanser Arasındaki İlişkilerin Tahminlenmesi

Abstract

Örtük ilaç-hastalık ilişkilerini tahmin etmek, yan etkileri ve geliştirme maliyetlerini en aza indirmek amacıyla yeni ilaçların geliştirilmesi için kritik öneme sahiptir. Var olan ilaç-hastalık tahmin yöntemleri tipik olarak ya tekli ya da çoklu ilaç-hastalık ağlarına odaklanmaktadır. Özellikle kanser araştırmalarında nanoparçacıklardaki son gelişmeler, toksik yan etkileri azaltarak biyoyararlanım ve farmakokinetikte gelişmeler göstermektedir. Bu nedenle, nanopartiküllerin ilaçlar ve hastalıklarla etkileşimi geliştirme aşamasında iyileşme eğilimindedir. Bu çalışmada, hedeflenen ilaç dağıtımı için bir bilgi tabanı olarak nanopartikül, ilaç ve kanser türleri arasındaki sınıf etkileşimlerini içeren hücreye özgü ilaç dağıtım verilerine varyasyonel bir çizge otokodlayıcı modeli sunmaktadır. Hücreye özgü ilaç verme verileri, ilişkilerin yalnızca bu sınıf etkileşimlerinin dizileri arasında var olduğu iki parçalı bir grafiğe dönüştürülür. Deneysel sonuçlar, bilgi çizgesi ile geliştirilmiş Varyasyonel Çizge Otokodlayıcı modelinin VGAE-ROC-AUC (0.9627) ve VGAE-AP (0.9566) skorlarıyla Çizge Otokodlayıcı modelinden daha iyi performans sergilediğini göstermektedir.

Keywords

• Varyasyonel Çizge Otokodlayıcı
• Nanoparçacıklar
• İlaç-hastalık İlişkisi

Prediction of Associations between Nanoparticle, Drug and Cancer Using Variational Graph Autoencoder

Abstract

Predicting implicit drug-disease associations is critical to the development of new drugs, with the aim of minimizing side effects and development costs. Existing drug-disease prediction methods typically focus on either single or multiple drug-disease networks. Recent advances in nanoparticles particularly in cancer research show improvements in bioavailability and pharmacokinetics by reducing toxic side effects. Thus, the interaction of the nanoparticles with drugs and diseases tends to improve during the development phase. In this study, it presents a variational graph autoencoder model to the cell-specific drug delivery data, including the class interactions between nanoparticle, drug, and cancer types as a knowledge base for targeted drug delivery. The cell-specific drug delivery data is transformed into a bipartite graph where relations only exist between sequences of these class interactions. Experimental results show that the knowledge graph enhanced Variational Graph Autoencoder model with VGAE-ROC-AUC (0.9627) and VGAE-AP (0.9566) scores performs better than the Graph Autoencoder model.

Keywords

• Variational Graph Autoencoder
• Nanoparticles
• Drug–disease Association

Thanks

We would like to thank Sumeyra Cigdem Sozer and Cigdem Karakoyun for their detailed feedback and suggestions during the adaptation of the publicly available dataset to the proposed method.

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