Prediction of Drug-Target Interactions Through the Application of Supervised Machine Learning Algorithms-Reference-Cited by-同舟云学术

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Prediction of Drug-Target Interactions Through the Application of Supervised Machine Learning Algorithms

Published:2023-12-07 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 International Conference on Emerging Research in Computational Science (ICERCS)
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Author:

Rafi D Mahammad¹,Chandrashekar Rakesh²,Veena CH³,Dutt Amit⁴,Waghmare Shwetambari Pandurang⁵,Yamini Supriya B⁶

Affiliation:

1. Institute of Aeronautical Engineering,Department of Computer Science and Engineering,Hyderabad,Telangana,India

2. New Horizon College of Engineering,Department of Mechanical Engineering,Bangalore

3. Institute of Aeronautical Engineering,Hyderabad,India

4. Lovely Professional University,Phagwara,India

5. Bharati Vidyapeeth College of Engineering,Department of Mathematic,Navi Mumbai,India

6. Koneru Lakshmaiah Education Foundation,Department of CSE,Vaddeswaram,AP,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10433896/10433898/10434245.pdf?arnumber=10434245

Reference20 articles.

1. A survey of drug-target interaction and affinity prediction methods via graph neural networks

2. BCM-DTI: A fragment-oriented method for drug–target interaction prediction using deep learning

3. Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions

4. DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions

5. DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network

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