Author:
Xu Mengdie,Zhao Xinwei,Wang Jingyu,Feng Wei,Wen Naifeng,Wang Chunyu,Wang Junjie,Liu Yun,Zhao Lingling
Abstract
AbstractDrug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug–Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations.
Funder
Graduate Research and Innovation Projects of Jiangsu Province
Jiangsu Science and Technology Department
the Industrial and Information Industry Transformation and Upgrading Special Fund of Jiangsu Province
the Key Project of Smart Jiangsu
Jiangsu Province Engineering Research Center of Big Data Application in Chronic Disease and Intelligent Health Service
2022 Nanjing Life and Health Science and Technology Special Project
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Computer Science Applications
Cited by
4 articles.
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