BayReL: Bayesian Relational Learning for Multi-omics Data Integration

• URL: http://arxiv.org/abs/2010.05895v3
• Date: Thu, 22 Oct 2020 07:22:40 GMT
• Title: BayReL: Bayesian Relational Learning for Multi-omics Data Integration
• Authors: Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Xiaoning Qian
• Abstract summary: We develop a novel method that infers interactions across different multi-omics data types. BayReL learns view-specific latent variables as well as a multi-partite graph that encodes the interactions across views. Our experiments on several real-world datasets demonstrate enhanced performance of BayReL.
• Score: 31.65670269480794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying molecular interactions across different data types helps reveal signal transduction mechanisms across different classes of molecules. In this paper, we develop a novel Bayesian representation learning method that infers the relational interactions across multi-omics data types. Our method, Bayesian Relational Learning (BayReL) for multi-omics data integration, takes advantage of a priori known relationships among the same class of molecules, modeled as a graph at each corresponding view, to learn view-specific latent variables as well as a multi-partite graph that encodes the interactions across views. Our experiments on several real-world datasets demonstrate enhanced performance of BayReL in inferring meaningful interactions compared to existing baselines.

Related papers

• Representation-Enhanced Neural Knowledge Integration with Application to Large-Scale Medical Ontology Learning [3.010503480024405]
  We propose a theoretically guaranteed statistical framework, called RENKI, to enable simultaneous learning of relation types. The proposed framework incorporates representation learning output into initial entity embedding of a neural network that approximates the score function for the knowledge graph. We demonstrate the effect of weighting in the presence of heterogeneous relations and the benefit of incorporating representation learning in nonparametric models.
  arXiv  Detail & Related papers  (2024-10-09T21:38:48Z)
• GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation [68.63955715643974]
  Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o) We propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o)
  arXiv  Detail & Related papers  (2024-07-08T01:06:13Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• MolTC: Towards Molecular Relational Modeling In Language Models [28.960416816491392]
  We propose a novel framework for Molecular inTeraction prediction following Chain-of-Thought (CoT) theory termed MolTC. Our experiments, conducted across various datasets involving over 4,000,000 molecular pairs, exhibit the superiority of our method over current GNN and LLM-based baselines.
  arXiv  Detail & Related papers  (2024-02-06T07:51:56Z)
• Graph Relation Distillation for Efficient Biomedical Instance Segmentation [80.51124447333493]
  We propose a graph relation distillation approach for efficient biomedical instance segmentation. We introduce two graph distillation schemes deployed at both the intra-image level and the inter-image level. Experimental results on a number of biomedical datasets validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-01-12T04:41:23Z)
• CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data [47.2764293508916]
  Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. One obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. We propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention.
  arXiv  Detail & Related papers  (2023-04-12T00:22:18Z)
• Molecular Joint Representation Learning via Multi-modal Information [11.493011069441188]
  We propose a novel framework of molecular joint representation learning via Multi-Modal information of SMILES and molecular Graphs, called MMSG. We improve the self-attention mechanism by introducing bond level graph representation as attention bias in Transformer. We further propose a Bidirectional Message Communication Graph Neural Network (BMC GNN) to strengthen the information flow aggregated from graphs for further combination.
  arXiv  Detail & Related papers  (2022-11-25T11:53:23Z)
• MoReL: Multi-omics Relational Learning [26.484803417186384]
  We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across heterogeneous views. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, but also increases the model flexibility with the distribution-based regularization.
  arXiv  Detail & Related papers  (2022-03-15T02:50:07Z)
• A Variational Information Bottleneck Approach to Multi-Omics Data Integration [98.6475134630792]
  We propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2021-02-05T06:05:39Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• A generalized kernel machine approach to identify higher-order composite effects in multi-view datasets [4.579719459619913]
  We propose a novel kernel machine approach to identify higher-order composite effects in multi-view biomedical datasets. The proposed method can effectively identify higher-order composite effects and suggest that corresponding features function in a concerted effort.
  arXiv  Detail & Related papers  (2020-04-29T08:56:02Z)

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.