Descriptive prediction of drug side‐effects using a hybrid deep learning model-Reference-Cited by-同舟云学术

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Descriptive prediction of drug side‐effects using a hybrid deep learning model

Published:2021-03 Issue:6 Volume:36 Page:2491-2510
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J Intell Syst

Author:

Lee Chun Yen¹^ORCID,Chen Yi‐Ping Phoebe¹^ORCID

Affiliation:

1. Department of Computer Science and Information Technology La Trobe University Melbourne Australia

Publisher

Wiley

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22389

Reference42 articles.

1. Large-scale prediction and testing of drug activity on side-effect targets

2. Evidence for Possible Involvement of 5-HT 2B Receptors in the Cardiac Valvulopathy Associated With Fenfluramine and Other Serotonergic Medications

3. HERG, a Primary Human Ventricular Target of the Nonsedating Antihistamine Terfenadine

4. Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network;Liu P;BMC Bioinf,2019

5. Modeling polypharmacy side effects with graph convolutional networks

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1. A survey of the recent trends in deep learning for literature based discovery in the biomedical domain;Neurocomputing;2024-02

2. mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events;Computers in Biology and Medicine;2024-01

3. Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images;IEEE Access;2024

4. Graph-level Anomaly Detection via Deep Metric Learning;2023 6th International Conference on Algorithms, Computing and Artificial Intelligence;2023-12-22

5. A Knowledge Graph-based Clustering Approach for Drug Side Effects Prediction*;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

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