Multi-Level Attention Graph Neural Network for Clinically Interpretable Pathway-Level Biomarkers Discovery

Xing, Xiaohan; Yang, Fan; Li, Hang; Zhang, Jun; Zhao, Yu; Huang, Junzhou; Meng, Max Q.-H.; Yao, Jianhua

doi:10.1101/2020.12.03.409755

Cited by 5 publications

(9 citation statements)

References 23 publications

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“…The MLA‐GNN has shown strong resilience to batch effects by imitating the biological regulatory process and pinpointing biomarkers at the pathway level. This approach significantly improves the precision and effectiveness in prediction tasks involving both transcriptomic and proteomic data 44 . Through the use of the full‐gradient graph saliency mechanism, the MLA‐GNN has the capacity to reveal clinically meaningful biomarkers at the pathway level, which, although pertinent, often remains undetected by conventional methods 44 .…”

Section: Methodsmentioning

confidence: 99%

“…This approach significantly improves the precision and effectiveness in prediction tasks involving both transcriptomic and proteomic data 44 . Through the use of the full‐gradient graph saliency mechanism, the MLA‐GNN has the capacity to reveal clinically meaningful biomarkers at the pathway level, which, although pertinent, often remains undetected by conventional methods 44 . In this study, the MLA‐GNN was utilized to construct the novel model based on 294 genes 44 (Table S2).…”

Section: Methodsmentioning

confidence: 99%

“…44 Through the use of the full-gradient graph saliency mechanism, the MLA-GNN has the capacity to reveal clinically meaningful biomarkers at the pathway level, which, although pertinent, often remains undetected by conventional methods. 44 In this study, the MLA-GNN was utilized to construct the novel model based on 294 genes 44 (Table S2). This approach involved identifying the intersection of differential genes at three levels: gene expression, M1 macrophage infiltration, and TIME-associated factors.…”

Section: Developing a Model For Predicting Ici Efficacy Using Deep Le...mentioning

confidence: 99%

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Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single‐cell RNA sequencing

Yu,

Chen,

Ouyang

et al. 2024

MedComm

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The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C‐X‐C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single‐cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune‐inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune‐related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune‐related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G‐related genes was developed using multi‐level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune‐active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Developing a Model For Predicting Ici Efficacy Using Deep Le...mentioning

confidence: 99%

See 1 more Smart Citation

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single‐cell RNA sequencing

Yu,

Chen,

Ouyang

et al. 2024

MedComm

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“…Xing et al [59] proposed a multi-level attention graph neural network (MLA-GNN) for multi-task prediction. As a first step in the model, the omics data (unimodal, e.g.…”

Section: Data Augmentation With Domain Knowledgementioning

confidence: 99%

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

Wysocka

Wysocki

Zufferey

et al. 2022

Preprint

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Background There is an increasing interest in the use of Deep Learning (DL) based methods as a supporting analytical framework in oncology. However, most direct applications of DL will deliver models with limited transparency and explainability, which constrain their deployment in biomedical settings. Methods This systematic review discusses DL models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. It focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. For this, we retrieved and analyzed 42 studies focusing on emerging architectural and methodological advances, the encoding of biological domain knowledge and the integration of explainability methods. Results We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We introduce a concept of bio-centric interpretability and according to its taxonomy, we discuss representational methodologies for the integration of domain prior knowledge in such models. Conclusions The paper provides a critical outlook into contemporary methods for explainability and interpretabiltiy used in DL for cancer. The analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability. We introduce bio-centric interpretability which is an important step towards formalisation of biological interpretability of DL models and developing methods that are less problem- or application-specific.

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“…Xing et al [53] proposed a multi-level attention graph neural network (MLA-GNN) for multi-task prediction. As a first step in the model, the omics data (unimodal, e.g.…”

Section: Data Enrichment and Augmentation Driven By Relations In The ...mentioning

confidence: 99%

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

Wysocka¹,

Wysocki²,

Zufferey³

et al. 2022

Preprint

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In this paper we provide a structured literature analysis focused on Deep Learning (DL) models used to support inference in cancer biology with a particular emphasis on multi-omics analysis. The work focuses on how existing models address the need for better dialogue with prior knowledge, biological plausibility and interpretability, fundamental properties in the biomedical domain. We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e.g. pathways or Protein-Protein-Interaction networks) and interpretability. This represents a fundamental functional shift towards models which can integrate mechanistic and statistical inference aspects. We discuss representational methodologies for the integration of domain prior knowledge in such models. The paper also provides a critical outlook into contemporary methods for explainability and interpretabiltiy. This analysis points in the direction of a convergence between encoding prior knowledge and improved interpretability.

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Multi-Level Attention Graph Neural Network for Clinically Interpretable Pathway-Level Biomarkers Discovery

Cited by 5 publications

References 23 publications

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single‐cell RNA sequencing

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single‐cell RNA sequencing

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data

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