Heterogeneous graph attention network improves cancer multiomics integration

• URL: http://arxiv.org/abs/2408.02845v1
• Date: Mon, 5 Aug 2024 22:01:13 GMT
• Title: Heterogeneous graph attention network improves cancer multiomics integration
• Authors: Sina Tabakhi, Charlotte Vandermeulen, Ian Sudbery, Haiping Lu,
• Abstract summary: We introduce a Heterogeneous Graph ATtention network for omics integration (HeteroGATomics) to improve cancer diagnosis. Experiments on three cancer multiomics datasets demonstrate HeteroGATomics' superior performance in cancer diagnosis.
• Score: 8.729516996214537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increase in high-dimensional multiomics data demands advanced integration models to capture the complexity of human diseases. Graph-based deep learning integration models, despite their promise, struggle with small patient cohorts and high-dimensional features, often applying independent feature selection without modeling relationships among omics. Furthermore, conventional graph-based omics models focus on homogeneous graphs, lacking multiple types of nodes and edges to capture diverse structures. We introduce a Heterogeneous Graph ATtention network for omics integration (HeteroGATomics) to improve cancer diagnosis. HeteroGATomics performs joint feature selection through a multi-agent system, creating dedicated networks of feature and patient similarity for each omic modality. These networks are then combined into one heterogeneous graph for learning holistic omic-specific representations and integrating predictions across modalities. Experiments on three cancer multiomics datasets demonstrate HeteroGATomics' superior performance in cancer diagnosis. Moreover, HeteroGATomics enhances interpretability by identifying important biomarkers contributing to the diagnosis outcomes.

Related papers

• Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification [40.45049709820343]
  Multi-omics data integration poses significant challenges due to the high dimensionality, data complexity, and distinct characteristics of various omics types. This study evaluates three graph neural network architectures for multi-omics (MO) integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN)
  arXiv  Detail & Related papers  (2024-10-05T16:17:44Z)
• LASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification [41.94295877935867]
  This paper introduces LASSO-MOGAT, a graph-based deep learning framework that integrates messenger RNA, microRNA, and DNA methylation data to classify 31 cancer types.
  arXiv  Detail & Related papers  (2024-08-30T16:26:04Z)
• The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges [101.83124435649358]
  Homophily principle, ie nodes with the same labels or similar attributes are more likely to be connected. Recent work has identified a non-trivial set of datasets where GNN's performance compared to the NN's is not satisfactory.
  arXiv  Detail & Related papers  (2024-07-12T18:04:32Z)
• Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis [7.996257103473235]
  We propose Pathology-Genome Heterogeneous Graph (PGHG) that integrates whole slide images (WSI) and bulk RNA-Seq expression data with heterogeneous graph neural network for cancer survival analysis. The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph. We evaluate the model on low-grade gliomas, glioblastoma, and kidney renal papillary cell carcinoma datasets from the Cancer Genome Atlas.
  arXiv  Detail & Related papers  (2024-04-11T09:07:40Z)
• IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration [2.0971479389679337]
  We introduce a novel integrative neural network approach for cancer molecular subtype and biomedical classification applications. IGCN can identify which types of omics receive more emphasis for each patient to predict a certain class. IGCN has the capability to pinpoint significant biomarkers from a range of omics data types.
  arXiv  Detail & Related papers  (2024-01-31T05:52:11Z)
• Integrate Any Omics: Towards genome-wide data integration for patient stratification [6.893309898200498]
  IntegrAO is an unsupervised framework for integrating incomplete multi-omics data and classifying new samples. IntegrAO's ability to handle heterogeneous and incomplete data makes it an essential tool for precision oncology.
  arXiv  Detail & Related papers  (2024-01-15T19:57:07Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Heterogeneous Similarity Graph Neural Network on Electronic Health Records [74.66674469510251]
  We propose Heterogeneous Similarity Graph Neural Network (HSGNN) to analyze EHRs with a novel heterogeneous GNN. Our framework consists of two parts: one is a preprocessing method and the other is an end-to-end GNN. The GNN takes all homogeneous graphs as input and fuses all of them into one graph to make a prediction.
  arXiv  Detail & Related papers  (2021-01-17T23:14:29Z)
• Heterogeneous Graph Neural Networks for Malicious Account Detection [64.0046412312209]
  We present GEM, the first heterogeneous graph neural network approach for detecting malicious accounts. We learn discriminative embeddings from heterogeneous account-device graphs based on two fundamental weaknesses of attackers, i.e. device aggregation and activity aggregation. Experiments show that our approaches consistently perform promising results compared with competitive methods over time.
  arXiv  Detail & Related papers  (2020-02-27T18:26:44Z)
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels [66.57101219176275]
  Disease diagnosis on chest X-ray images is a challenging multi-label classification task. We propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases. Our method is the first to build a graph over the feature maps with a dynamic adjacency matrix for correlation learning.
  arXiv  Detail & Related papers  (2020-02-26T17:10:48Z)

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.