MoReL: Multi-omics Relational Learning

• URL: http://arxiv.org/abs/2203.08149v1
• Date: Tue, 15 Mar 2022 02:50:07 GMT
• Title: MoReL: Multi-omics Relational Learning
• Authors: Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Xiaoning Qian
• Abstract summary: 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.
• Score: 26.484803417186384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems. One of critical challenges when dealing with real-world multi-omics data is that they may manifest heterogeneous structures and data quality as often existing data may be collected from different subjects under different conditions for each type of omics data. We propose a novel deep Bayesian generative model to efficiently infer a multi-partite graph encoding molecular interactions across such heterogeneous views, using a fused Gromov-Wasserstein (FGW) regularization between latent representations of corresponding views for integrative analysis. With such an optimal transport regularization in the deep Bayesian generative model, it not only allows incorporating view-specific side information, either with graph-structured or unstructured data in different views, but also increases the model flexibility with the distribution-based regularization. This allows efficient alignment of heterogeneous latent variable distributions to derive reliable interaction predictions compared to the existing point-based graph embedding methods. Our experiments on several real-world datasets demonstrate enhanced performance of MoReL in inferring meaningful interactions compared to existing baselines.

Related papers

• Learning Divergence Fields for Shift-Robust Graph Representations [73.11818515795761]
  In this work, we propose a geometric diffusion model with learnable divergence fields for the challenging problem with interdependent data. We derive a new learning objective through causal inference, which can guide the model to learn generalizable patterns of interdependence that are insensitive across domains.
  arXiv  Detail & Related papers  (2024-06-07T14:29:21Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space [45.418113011182186]
  This study proposes a novel method to address limitations by combining Variational AutoEncoders (VAEs) with a Factor Analysis latent space (FA-VAE) The proposed FA-VAE method employs multiple VAEs to learn a private representation for each heterogeneous data view in a continuous latent space.
  arXiv  Detail & Related papers  (2022-07-19T10:46:02Z)
• Scalable Regularised Joint Mixture Models [2.0686407686198263]
  In many applications, data can be heterogeneous in the sense of spanning latent groups with different underlying distributions. We propose an approach for heterogeneous data that allows joint learning of (i) explicit multivariate feature distributions, (ii) high-dimensional regression models and (iii) latent group labels. The approach is demonstrably effective in high dimensions, combining data reduction for computational efficiency with a re-weighting scheme that retains key signals even when the number of features is large.
  arXiv  Detail & Related papers  (2022-05-03T13:38:58Z)
• Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets [53.34152466646884]
  In this paper, we show how bringing recent results on equivariant representation learning instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. We demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples.
  arXiv  Detail & Related papers  (2022-03-29T04:54:06Z)
• A graph representation based on fluid diffusion model for multimodal data analysis: theoretical aspects and enhanced community detection [14.601444144225875]
  We introduce a novel model for graph definition based on fluid diffusion. Our method is able to strongly outperform state-of-the-art schemes for community detection in multimodal data analysis.
  arXiv  Detail & Related papers  (2021-12-07T16:30:03Z)
• 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)
• BayReL: Bayesian Relational Learning for Multi-omics Data Integration [31.65670269480794]
  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.
  arXiv  Detail & Related papers  (2020-10-12T17:43:07Z)
• 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)
• Bayesian Sparse Factor Analysis with Kernelized Observations [67.60224656603823]
  Multi-view problems can be faced with latent variable models. High-dimensionality and non-linear issues are traditionally handled by kernel methods. We propose merging both approaches into single model.
  arXiv  Detail & Related papers  (2020-06-01T14:25:38Z)

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.