کنفرانس مهندسی برق ایران

صفحه اصلی / سی و یکمین کنفرانس بین المللی مهندسی برق

MAD-TI: Meta-path Aggregated-Graph Attention Network for Drug Target Interaction Prediction

نویسندگان :

Reza Shami Tanha¹ Maryam Sadighian² Arash Zabihian³ Mohsen Hooshmand⁴ Mohsen Afsharchi⁵

1- دانشگاه تحصیلات تکمیلی علوم پایه زنجان 2- دانشگاه تحصیلات تکمیلی علوم پایه زنجان 3- دانشگاه تهران 4- دانشگاه تحصیلات تکمیلی علوم پایه زنجان 5- دانشگاه زنجان

کلمات کلیدی :

Drug Target Interaction،Graph Neural Networks،Meta-path،Attention mechanism،Graph Attention Network،Bioinformatics

چکیده :

Computational identification of unknown drugtarget interactions (DTI) is crucial in locating new drug treatments for proteins, viruses, and diseases. This work proposes MAD-TI a meta-path-based, GAT-oriented method to predict DTIs. Our proposed method uses a heterogeneous graph of drugs, targets, diseases, and side effects as the input graph. Then, it applies two graph attention networks to generate the embeddings of drugs and targets. Using the embeddings, it predicts the unknown DTIs. The results show that MAD-TI outperforms the state-of-the-art methods.

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