Predicting Drug Response Based on Multi-Omics Fusion and Graph Convolution

Peng, Wei; Chen, Tielin; Dai, Wei

doi:10.1109/jbhi.2021.3102186

Cited by 55 publications

(24 citation statements)

References 31 publications

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“…In multi-omics, graph convolutional neural networks have had various applications. For example, ( Peng et al , 2022 ) used them for predicting cancer drug response, and ( Wang, 2021 ; Zhang, 2021 ) used them to do biomedical classifications (e.g. Alzheimer’s disease diagnosis and pan-cancer classification, respectively).…”

Section: Discussionmentioning

confidence: 99%

Dealing with dimensionality: the application of machine learning to multi-omics data

et al. 2023

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Motivation Machine learning (ML) methods are motivated by the need to automate information extraction from large data sets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets. Results Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas data set, which is accessible and contains many diverse experiments. Availability All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807 Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

Dealing with dimensionality: the application of machine learning to multi-omics data

et al. 2023

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“…CTP heterogeneous networks are complex heterogeneous networks containing three types of edges (relations), namely C-T, T-T and T-P. For each CTP heterogeneous network, we first used the basic graph neural network model to pass messages to the nodes under each relation, and then used the heterogeneous graph convolution module [35] to aggregate the messages of the nodes under all relations by summation, with two layers of the network.…”

Section: ) Graph Neural Network Modelmentioning

confidence: 99%

Application of a Complex Network Modeling Approach to Explore the Material Basis and Mechanisms of Traditional Chinese Medicine: A Case Study of Xuefu Zhuyu Decoction for the Treatment of Two Types of Angina Pectoris

Zhang

et al. 2022

IEEE Access

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The complex processes of action of Chinese medicines and their formulations can be studied in terms of complex heterogeneous networks, however multiple diseases are different from one disease. In this study, we proposed a graph neural network-based modelling strategy to explore the material basis and mechanisms of the formulae under multiple disease conditions, exemplified by Xuefu Zhuyu Decoction for stable angina pectoris and unstable angina pectoris. Our modelling approach consists of two parts, a feature part and a model part. In the feature part, we calculated four similarities and their combinations in the similarity network as features of nodes of the heterogeneous network, using a variety of algorithms. In the model part, both the multi-label classification model for compounds and the link prediction model for compounds-targets were performed in six model comparison experiments and four partial ablation experiments. We confirmed that the GraphSAGEbased model was the best performing multi-label classification model while the GAT-based model was the best performing link prediction model, and that the full model with various similarity contents was optimal. Good generalization ability of the model was demonstrated through generalization ability experiments. In the case study, the model was used to predict potential compound-target interactions for marker targets and the reliability of the predictions was demonstrated by molecular docking and literature mining. In conclusion, the model successfully predicted the known material basis and mechanisms and also predicted unknown compound-marker target interactions. The proposed modelling approach can be extended to the study of other formulations. INDEX TERMSHeterogeneous network; material basis; mechanism; multi-label classification; link prediction I. INTRODUCTION Chinese herbs and their constituent formulas are widely used in clinical practice to treat various types of diseases[1, 2], yet the scientific and systematic elucidation of the material basis and mechanism of action of Chinese herbs and formulas has been a challenge that This article has been accepted for publication in IEEE Access.

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“…The attention-based model captures the long-range dependency among input entities, enabling efficient neural solutions for non-Euclidean data and molecular structures. Graph Neural Networks (GNNs) introduce the community structure to multi-omic data by similarity measurement between observations, which empirically hardens the predictive performance of cancer phenotype identification [19][20][21][22][23] or treatment outcome modeling 24,25 . To this point, we observe that translation between biological problems and current methodologies is crucial for advancing AI to the new generation of cancer research, which can be done by studying the issue from a microscopic point of view.…”

Section: Introductionmentioning

confidence: 99%

Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology

Nguyen¹,

Chen²

2022

Preprint

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Quantifying the dynamics of tumor burden reveals useful information about cancer evolution concerning treatment effects and drug resistance, which play a crucial role in advancing model-informed drug developments (MIDD) towards personalized medicine and precision oncology. The emergence of Quantum Machine Intelligence offers unparalleled insights into tumor dynamics via a quantum mechanics perspective. This paper introduces a novel hybrid quantum-classical neural architecture named η−Net that enables quantifying quantum dynamics of tumor burden concerning treatment effects. We evaluate our proposed neural solution on two major use cases, including cohort-specific and patient-specific modeling. In silico numerical results show a high capacity and expressivity of η−Net to the quantified biological problem. Moreover, the close connection to representation learning -the foundation for successes of modern AI, enables efficient transferability of empirical knowledge from relevant cohorts to targeted patients. Finally, we leverage Bayesian optimization to quantify the epistemic uncertainty of model predictions, paving the way for η−Net towards reliable AI in decision-making for clinical usages.

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Predicting Drug Response Based on Multi-Omics Fusion and Graph Convolution

Cited by 55 publications

References 31 publications

Dealing with dimensionality: the application of machine learning to multi-omics data

Dealing with dimensionality: the application of machine learning to multi-omics data

Application of a Complex Network Modeling Approach to Explore the Material Basis and Mechanisms of Traditional Chinese Medicine: A Case Study of Xuefu Zhuyu Decoction for the Treatment of Two Types of Angina Pectoris

Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology

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