CancerGPT for few shot drug pair synergy prediction using large pretrained language models
Large language models (LLMs) have been shown to have significant potential in few-shot
learning across various fields, even with minimal training data. However, their ability to …
learning across various fields, even with minimal training data. However, their ability to …
MPCLCDA: predicting circRNA–disease associations by using automatically selected meta-path and contrastive learning
W Liu, T Tang, X Lu, X Fu, Y Yang… - Briefings in …, 2023 - academic.oup.com
Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying
the associations between human diseases and circRNA can help in disease prevention …
the associations between human diseases and circRNA can help in disease prevention …
Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves
administering two or more anti-cancer agents to increase efficacy and overcome multidrug …
administering two or more anti-cancer agents to increase efficacy and overcome multidrug …
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and
applications, and thus investigation of deep learning for HOIs has become a valuable …
applications, and thus investigation of deep learning for HOIs has become a valuable …
A review on graph neural networks for predicting synergistic drug combinations
M Besharatifard, F Vafaee - Artificial Intelligence Review, 2024 - Springer
Combinational therapies with synergistic effects provide a powerful treatment strategy for
tackling complex diseases, particularly malignancies. Discovering these synergistic …
tackling complex diseases, particularly malignancies. Discovering these synergistic …
MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders
P Zhang, S Tu - PLoS computational biology, 2023 - journals.plos.org
Accurate prediction of synergistic effects of drug combinations can reduce the experimental
costs for drug development and facilitate the discovery of novel efficacious combination …
costs for drug development and facilitate the discovery of novel efficacious combination …
A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks
The powerful combination of large-scale drug-related interaction networks and deep
learning provides new opportunities for accelerating the process of drug discovery …
learning provides new opportunities for accelerating the process of drug discovery …
A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs
W Wang, G Yuan, S Wan, Z Zheng, D Liu… - Briefings in …, 2024 - academic.oup.com
Combination therapy has exhibited substantial potential compared to monotherapy.
However, due to the explosive growth in the number of cancer drugs, the screening of …
However, due to the explosive growth in the number of cancer drugs, the screening of …
PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction
X Zhao, J Xu, Y Shui, M Xu, J Hu, X Liu, K Che… - Journal of …, 2024 - Springer
Motivation Drug combination therapies have shown promise in clinical cancer treatments.
However, it is hard to experimentally identify all drug combinations for synergistic interaction …
However, it is hard to experimentally identify all drug combinations for synergistic interaction …
Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel
Adverse drug reactions include side effects, allergic reactions, and secondary infections.
Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug …
Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug …