Predicting drug responses from extended-connectivity fingerprints (ECFPs) of drugs by using graph neural networks
Cancer is one of the leading causes of deaths and one of the treatments of cancer cure is the use of drugs that inhibits the growth of cancer tumors. Hance, it is essential to be able to predict the response of cancer drugs to assess the effectiveness of each drug in slowing down the growth of abnor...
Saved in:
Main Author: | Asok Kumar Gaurav |
---|---|
Other Authors: | Jagath C Rajapakse |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/156576 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Predicting drug responses from drug chemical features by using deep neural networks
by: Krithika Ramamoorthy
Published: (2021) -
Graph neural network with knowledge graph
by: Ang, Qi Xuan
Published: (2020) -
Graph neural networks
by: Lian, Ran
Published: (2023) -
Graph attention networks and approximate personalized propagation of neural prediction models for unsupervised graph representation learning
by: Bharadwaja, Tanay
Published: (2022) -
Pre-training graph neural networks for link prediction in biomedical networks
by: Long, Yahui, et al.
Published: (2022)