BayReL learns molecular interactions across multi-omics data.
problem Inferring meaningful interactions across diverse molecular data types.
method BayReL uses Bayesian representation learning with graph models to integrate multi-omics data.
result BayReL outperforms existing methods in inferring molecular interactions.
Multi-omic data provides multiple views of the same patients. Integrative analysis of multi-omic data is crucial to elucidate the molecular underpinning of disease etiology. However, multi-omic data has the "big p, small N" problem (the number of features is large, but the number of samples is small), it is challenging…
RobKMR improves robustness in multi-omics data analysis for osteoporosis biomarker discovery.
problem Sensitivity to adversarial outliers and lack of comprehensive multi-omics data integration.
method RobKMR, a non-linear M-estimator-based approach using robust kernel centered Gram matrix and robust score test.
result Selected biomarkers (DKK1, MTND5, FASTKD2) significantly bond with four drugs for osteoporosis.
MOTGNN integrates multi-omics data for disease classification with improved accuracy and interpretability.
problem Challenges in integrating multi-omics data due to high dimensionality, heterogeneity, and lack of reliable interaction networks.
method MOTGNN uses XGBoost for graph construction, modality-specific GNNs for representation learning, and a deep feedforward network for cross-omics integration.
result MOTGNN outperforms state-of-the-art baselines by 5-10% in accuracy, ROC-AUC, and F1-score across three real-world disease datasets.
MKL-based models outperform complex multi-omics integrative approaches.
problem Integrating diverse omics data sources.
method Supervised multiple kernel learning with different kernel fusion strategies.
result MKL-based models outperform more complex architectures.
Study evaluates multi-omics data's role in predicting cancer survival.
problem Determining the usefulness of multi-omics data for predicting disease outcomes.
method 5-fold cross-validation with 12 prediction methods applied to 18 cancer datasets.
result Multi-omics data generally improves prediction performance, but not consistently.
Improved logistic regression for multi-omics data improves prediction and variable selection.
problem Predicting binary class labels from multi-omics datasets with varying characteristics.
method Two-step penalized logistic regression with separate variable selection for each data layer.
result Our approach selects more relevant predictors and achieves comparable prediction performance.
In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . T…
Estimates complex dependency structures in multi-omics data.
problem Graphical model estimation from multi-omics data with scalability and consistency.
method Pseudolikelihood-based graphical model framework with ℓ1-penalized empirical risk. result Estimates partial correlation network from dual-omic liver cancer data.
AI framework uses multi-omics data to personalize cancer treatment suggestions.
problem Leveraging AI for personalized cancer treatment based on complex patient characteristics.
method Modular machine learning framework trained on diverse multi-omics technologies.
result Superior performance in personalized counterfactual treatment suggestions.
MoReL models multi-omics data to find hidden molecular interactions.
problem Heterogeneous multi-omics data with varying quality and structure.
method Fused Gromov-Wasserstein (FGW) regularization in a deep Bayesian generative model.
result Enhanced performance in inferring meaningful interactions from real-world datasets.
BSFP method reveals latent patterns in multi-omic data for predicting lung function in HIV-associated OLD.
problem Limited understanding of multi-omic molecular phenomena and clinical outcomes in obstructive lung disease.
method Bayesian Simultaneous Factorization and Prediction (BSFP) method for multi-omic data, accommodating imputation and full posterior inference.
result BSFP reveals distinct clusters of patients with OLD and multi-omic patterns related to lung function decline.
Study evaluates consistency of feature attribution in deep learning for multi-omics data.
problem Challenges in interpretability of deep learning models in biological research.
method Investigation of Shapley Additive Explanations (SHAP) on multi-view deep learning models applied to multi-omics data.
result SHAP rankings are sensitive to architecture and random initialization, suggesting caution.
Survey examines challenges and tools for integrating multiple types of omics data.
problem Integrating multiple types of omics data for healthcare applications.
method Categorizes fusion approaches, collects open-source tools, explores datasets.
result Identifies challenges and gaps in multimodal learning for multi-omics.
Omics-GAN uses GANs to generate synthetic multi-omics data for improved disease prediction.
problem Limited sample sizes, noise, and heterogeneity in multi-omics data reduce predictive power.
method Omics-GAN is a GAN-based framework that generates high-quality synthetic multi-omics profiles.
result Synthetic datasets consistently improved prediction accuracy compared to original omics profiles.
Bayesian Cox model identifies biomarkers from multi-omics data.
problem Produce interpretable survival prognosis from multi-omics data.
method Penalized semiparametric Bayesian Cox model with graph-structured selection priors.
result Model identifies new biomarkers and improves survival prediction.
GmGM models multi-axis data for faster analysis.
problem Efficiently modeling multi-axis data across multiple tensors.
method Generalizes Gaussian Graphical Model to learn sparse graph representations across shared axes.
result Achieves significant speedup (order of magnitude) for large multi-modal datasets.
ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.
problem Causal inference in high-dimensional multi-omics data, especially when ignoring the hierarchical structure.
method Two-tiered divide-and-conquer strategy with ancestral conditioning sets.
result Achieves polynomial-time complexity and accurately recovers ancestral relationships.
Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of …
Paper proposes scalable method for analyzing multi-omic data.
problem Integrating high-dimensional multi-omic data for cancer subtyping.
method Mixed graphical model approach using Birth-Death MCMC algorithm.
result Our method outperforms LASSO and standard BDMCMC in computational efficiency and model selection accuracy.
scICML integrates multi-omics data from single cells using co-clustering.
problem High noise and sparsity in multi-omics data from single cells.
method Information-theoretic co-clustering-based multi-view learning.
result Improves clustering performance and provides biological insights.
Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computat…
A method for identifying joint and individual subspaces from multi-view data.
problem Unclear conditions for reliably identifying joint and individual subspaces from noisy, high-dimensional measurements.
method Rigorously quantifies conditions based on signal rank, principal angles, and noise levels. Characterizes spectrum perturbations of product of projection matrices.
result Estimates joint and individual subspaces more accurately than existing approaches in simulations and real-world applications.
In the integrative analyses of omics data, it is often of interest to extract data representation from one data type that best reflect its relations with another data type. This task is traditionally fulfilled by linear methods such as canonical correlation analysis (CCA) and partial least squares (PLS). However, infor…
Pipeline integrates cross-sectional and longitudinal multi-omics data for IBD research.
problem Integrating diverse data types from the same individuals for disease understanding.
method Statistical and deep learning methods for variable selection, feature extraction, and joint integration.
result Identified microbial pathways, metabolites, and genes discriminating IBD status.
New method learns complex cell networks from millions of cells.
problem Existing methods fail to scale to large datasets.
method Multi-axis Gaussian graphical models.
result Method scales to millions of cells in minutes.
Federated learning improves bioinformatics by sharing data legally.
problem Lack of access to diverse data in bioinformatics.
method Combines data from multiple institutions legally.
result Federated learning accelerates clinical discovery and robust exploration.
New Gromov-Wasserstein metric controls rigidity and incorporates prior knowledge.
problem Inflexible Gromov-Wasserstein distance and lack of feature alignment.
method Augmented Gromov-Wasserstein distance with feature alignments and prior knowledge.
result Improved performance in single-cell multi-omic alignment and transfer learning.
SiMLR reduces complex biomedical data into simpler, interpretable forms.
problem Handling high-dimensional biomedical data for better understanding and prediction.
method Similarity-driven multi-view linear reconstruction (SiMLR) with novel objective function and regularization.
result SiMLR outperforms other methods in various biomedical datasets.
IndiSeek learns disentangled representations by balancing independence and completeness.
problem Learning disentangled representations with mutual information in multi-modal data.
method Combines independence-enforcing objective with a reconstruction loss that bounds conditional mutual information.
result Demonstrates effectiveness on synthetic data, CITE-seq, and real-world multi-modal benchmarks.
New method disentangles shared and private latent factors in multimodal data.
problem Challenges in disentangling shared and private latent factors in multimodal data.
method Proposes a modification to existing multimodal Variational Autoencoders (MMVAE) to better handle modality-specific variation.
result Demonstrates improved robustness of modified MMVAE to modality-specific variation.
New method estimates covariance in multi-view data with better accuracy and uncertainty.
problem Estimating covariance in multi-view data with shared and view-specific latent factors.
method Spectral decompositions and conditional conjugate priors for factor loadings and residual variances.
result Proves favorable asymptotic properties and excellent performance in simulations and real data.
We introduce Block Sparse Canonical Correlation Analysis which estimates multiple pairs of canonical directions (together a "block") at once, resulting in significantly improved orthogonality of the sparse directions which, we demonstrate, translates to more interpretable solutions. Our approach builds on the sparse CC…
For precision medicine and personalized treatment, we need to identify predictive markers of disease. We focus on Alzheimer's disease (AD), where magnetic resonance imaging scans provide information about the disease status. By combining imaging with genome sequencing, we aim at identifying rare genetic markers associa…
This abstract reviews recent methods for predicting protein-ligand binding affinity.
problem Predicting protein-ligand binding affinity for various applications in life sciences.
method Traditional and deep learning models for binding affinity prediction.
result Improved predictive performance of AI-driven models.
Multi-view data, that is matched sets of measurements on the same subjects, have become increasingly common with advances in multi-omics technology. Often, it is of interest to find associations between the views that are related to the intrinsic class memberships. Existing association methods cannot directly incorpora…
New methods integrate nonlinear, sparse, and multi-view aspects for high-dimensional data analysis.
problem Integrating nonlinear dependence, sparsity, and multi-view data in high-dimensional datasets.
method Proposes HSIC-SGCCA, SA-KGCCA, and TS-KGCCA methods for multi-view high-dimensional data analysis.
result HSIC-SGCCA outperforms competing methods in multi-view variable selection.
New methods for visualizing multi-view data improve clustering accuracy.
problem Visualizing multi-view data for better understanding of samples.
method Extensions of manifold learning methods (t-SNE, LLE, ISOMAP) for multi-view data.
result Improved clustering accuracy of multi-view data.
Proposes HeteroJIVE for joint subspace estimation in multi-view data with statistical and structural heterogeneity.
problem Joint subspace estimation in multi-view data with varying statistical and structural heterogeneity.
method HeteroJIVE: A weighted two-stage spectral algorithm addressing statistical and structural heterogeneity.
result HeteroJIVE achieves the O(K−1/2) rate without iterative refinement, validating the oracle-optimal weighting scheme. Unified framework for higher-order network analysis.
problem Complex structure of space of networks.
method Measure-theoretic formalism, Gromov-Wasserstein distance, co-optimal transport distance.
result Unified theoretical treatment of generalized networks.
fiBAG integrates multiplatform genomic data to identify disease markers.
problem Understanding complex mechanisms underlying human diseases from multiplatform genomic data.
method fiBAG uses Gaussian process models and Bayes factors to identify functional evidence and guide variable selection.
result fiBAG improves detection of disease-related markers compared to non-integrative methods.
Bayesian method improves sparse CCA for multi-view data.
problem Integrative statistical analysis of multi-view high-dimensional data.
method Bayesian infinite factor model with graphical horseshoe prior or diagonal structure to encourage sparsity.
result The proposed Bayesian ScSCCA approach achieves robust estimation of sparse CCA.
Unified method for discovering biclusters and triclusters in longitudinal data.
problem High-dimensional, sparsely sampled, irregularly observed longitudinal data.
method Tri-SfSVD, a unified sparse functional Singular Value Decomposition framework.
result Identified localized structures at the subject, subject-feature, and subject-feature-time levels.
A new framework for PPLS combines noise estimation, optimization, and calibration.
problem Probabilistic PLS models need interpretable latent factors and calibrated uncertainty.
method End-to-end pipeline combining noise estimation, constrained optimization, and prediction calibration.
result Achieves near-nominal coverage and native calibrated uncertainty across benchmarks.
Bayesian models link multiview data to outcomes.
problem Inferring relationships between diverse data types and outcomes.
method Developed two factor regression models: JFR and JAFAR.
result Improved prediction of clinical outcomes from multi-omics data.
A new method for analyzing multi-source, multi-way data reduces dimensionality and reveals shared and individual structures.
problem Analyzing multi-source, multi-way data from different high-throughput technologies.
method Multiple Linked Tensor Factorization (MULTIFAC) extending CP decomposition with L2 penalties and EM algorithm for incomplete data.
result MULTIFAC approximates underlying signal, identifies shared and unshared structures, and imputes missing data.
GPCCA integrates multi-modal data with missing values, improving clustering accuracy.
problem Integrating and analyzing multi-modal data with missing values and partial observations.
method Generalized Probabilistic Canonical Correlation Analysis (GPCCA) for unsupervised multi-modal data integration and dimensionality reduction.
result GPCCA outperforms existing methods in capturing essential patterns across modalities and provides robust low-dimensional embeddings.
Proposes a novel method to identify complex effects in multi-view datasets.
problem Challenges in analyzing multi-view biomedical datasets with complex interactions.
method Generalized kernel machine approach considering marginal and joint effects of features from different views.
result Effective identification of higher-order composite effects in multi-view datasets.