İlaç-İlaç Etkileşimlerini Keşfetmek: Bir Ağ Analizi ve Görselleştirme Yaklaşımı

Year 2023, Volume: 4 Issue: 1, 257 - 270, 26.06.2023

İlhan Uysal , Utku Köse

https://doi.org/10.55546/jmm.1268369

Abstract

Bu makale, ağ analizi ve görselleştirme yoluyla ilaç-ilaç etkileşimlerinin karmaşıklığını araştırmaktadır. İlaç-ilaç etkileşimlerini analiz etmek ve ilaçlar arasındaki ilişkileri keşfederek etkileşimli bir görselleştirme aracı sağlamak için ağ tabanlı bir yaklaşım sunulmaktadır. Ağ tabanlı yaklaşım, büyük bir ilaç-ilaç etkileşimi veri kümesine uygulanmakta ve ortaya çıkan ağın özelliklerini analiz etmektedir. Ayrıca, ilaç-ilaç etkileşimlerinin daha fazla araştırılması için ağ tabanlı yaklaşımın potansiyeli de tartışılmaktadır. Son olarak, ilaçlar arasındaki ilişkileri keşfetmek için etkileşimli bir görselleştirme aracı sağlayarak ağ tabanlı yaklaşımın etkinliği gösterilmektedir. Bu çalışmanın sonuçları, ilaç-ilaç etkileşimlerinin karmaşıklığının daha iyi anlaşılmasını sağlayacağı öngörülmekte ve ilaç keşfi ve geliştirmede ağ analizi ve görselleştirmenin potansiyel uygulamalarını önermektedir. Aynı zamanda kullanıcıların web uygulamasını ziyaret edebilmeleri ve grafiklerle doğrudan etkileşim kurabilmeleri için Pyvis ağ grafiklerini çevrimiçi olarak https://iuysal1905-streamlit-pyvis-network-app2-91q9sv.streamlit. app adresinde yayınlanmıştır.

Keywords

İlaç-ilaç etkileşimi, Ağ analizi, Görselleştirme.

Thanks

Bu çalışma İlhan Uysal’ın “Açıklanabilir Derin Öğrenme Modelleri ile İlaç Yeniden Konumlandırılması İçin Benzetim Ortamları Geliştirilmesi” başlıklı doktora tezi ile bağlantılıdır.

Exploring Drug-Drug Interactions: A Network Analysis and Visualization Approach

Abstract

This article investigates the complexity of drug-drug interactions through network analysis and visualization. A network-based approach is presented to analyze drug-drug interactions and provide an interactive visualization tool by exploring relationships between drugs. The network-based approach is applied to a large drug-drug interaction dataset and the properties of the resulting network are analyzed. The potential of the network-based approach for further exploration of drug-drug interactions is also discussed. Finally, the effectiveness of the network-based approach is demonstrated by providing an interactive visualization tool to discover relationships between drugs. The results of this study are expected to facilitate a better understanding of the complexity of drug-drug interactions and suggest potential applications of network analysis and visualization in drug discovery and development. It has also published Pyvis network graphs online at https://iuysal1905-streamlit-pyvis-network-app2-91q9sv.streamlit.app so that users can visit the web application and interact with the graphs directly.

Keywords

Drug-drug interaction, Network analysis, Visualization.

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