drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network
- URL: http://arxiv.org/abs/2405.08979v1
- Date: Tue, 14 May 2024 22:16:52 GMT
- Title: drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network
- Authors: Yoshitaka Inoue, Hunmin Lee, Tianfan Fu, Augustin Luna,
- Abstract summary: drGAT is a graph deep learning model that can predict sensitivity to drugs.
drGAT has superior performance over existing models, achieving 78% accuracy and 76% F1 score for 269 DNA-damaging compounds.
Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.
- Score: 9.637695046701493
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78\% accuracy (and precision), and 76\% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.
Related papers
- Regressor-free Molecule Generation to Support Drug Response Prediction [83.25894107956735]
Conditional generation based on the target IC50 score can obtain a more effective sampling space.
Regressor-free guidance combines a diffusion model's score estimation with a regression controller model's gradient based on number labels.
arXiv Detail & Related papers (2024-05-23T13:22:17Z) - Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural
Network with Biomedical Network [69.16939798838159]
We propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs.
EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths.
Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
arXiv Detail & Related papers (2023-11-15T06:34:00Z) - Causal Intervention for Measuring Confidence in Drug-Target Interaction
Prediction [17.91458766354762]
We focus on the problem of drug-target interactions, with knowledge mapping as the core technology.
A causal intervention-based confidence measure is employed to assess the triplet score to improve the accuracy of the drug-target interaction prediction model.
arXiv Detail & Related papers (2023-05-31T13:13:45Z) - Drug Synergistic Combinations Predictions via Large-Scale Pre-Training
and Graph Structure Learning [82.93806087715507]
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation.
Deep learning models have emerged as an efficient way to discover synergistic combinations.
Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
arXiv Detail & Related papers (2023-01-14T15:07:43Z) - DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm [0.521420263116111]
We introduce a Graph Neural Network (textitGNN) based model for drug synergy prediction.
In contrast to conventional models, our GNN-based approach learns task-specific drug representations directly from the graph structure of the drugs.
Our work suggests that learning task-specific drug representations and leveraging a diverse dataset is a promising approach to advancing our understanding of drug-drug interaction and synergy.
arXiv Detail & Related papers (2022-10-03T10:16:29Z) - SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity
Prediction [127.43571146741984]
Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery.
wet experiments remain the most reliable method, but they are time-consuming and resource-intensive.
Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue.
We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
arXiv Detail & Related papers (2022-06-20T14:53:25Z) - Improved Drug-target Interaction Prediction with Intermolecular Graph
Transformer [98.8319016075089]
We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture.
Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively.
IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
arXiv Detail & Related papers (2021-10-14T13:28:02Z) - Graph2MDA: a multi-modal variational graph embedding model for
predicting microbe-drug associations [7.149873402253933]
Microbes have become novel targets for the development of antibacterial agents.
screening of microbe-drug associations can benefit greatly drug research and development.
We propose a novel method, Graph2MDA, to predict microbe-drug associations.
arXiv Detail & Related papers (2021-08-14T07:33:05Z) - DeepDDS: deep graph neural network with attention mechanism to predict
synergistic drug combinations [0.9854322576538699]
computational screening has become an important way to prioritize drug combinations.
DeepDDS was superior to competitive methods by more than 16% predictive precision.
arXiv Detail & Related papers (2021-07-06T08:25:43Z) - Relational graph convolutional networks for predicting blood-brain
barrier penetration of drug molecules [12.041672273431994]
The evaluation of the BBB penetrating ability of drug molecules is a critical step in brain drug development.
We employ the relational graph convolutional network (RGCN) to handle the drug-protein relations as well as the features of each individual drug.
The performance was already promising, demonstrating the significant role of the drug-protein/drug relations in the prediction of BBB permeability.
arXiv Detail & Related papers (2021-07-04T15:56:02Z) - Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug
Response [49.86828302591469]
In this paper, we apply transfer learning to the prediction of anti-cancer drug response.
We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset.
The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
arXiv Detail & Related papers (2020-05-13T20:29:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.