BIDIFAC+ factorizes linked matrices for cancer studies.
problem Integrating multiple omics platforms across various cancer types.
method Flexible approach to simultaneous factorization and decomposition of linked matrices using BIDIFAC+.
result Identifies shared and specific modes of variability across multiple omics platforms and cancer types.
Modeling correlated mutations in cancer for personalized treatment.
problem Identifying mutations for personalized cancer therapy in heterogeneous profiles.
method Proposed correlated zero-inflated negative binomial process with mixed beta-Bernoulli and variational inference.
result Identified biologically relevant correlations between somatic mutations.
A new method combines multiple cancer datasets to improve analysis.
problem Combining multiple cancer datasets for comprehensive analysis.
method Multiple Augmented Reduced Rank Regression (maRRR) method.
result Improved power and insights from combining multiple cancer datasets.
OmiVAE combines variational autoencoders and a classification network to classify multi-omics data.
problem Classifying samples from high-dimensional multi-omics data.
method OmiVAE integrates variational autoencoders and a classification network to extract features and classify samples.
result OmiVAE achieved an average classification accuracy of 97.49% across 33 tumour types and normal samples.
The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However,…
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…
Model predicts anti-cancer drug responses using gene and molecular data.
problem Expensive and time-consuming cancer drug discovery and tailoring.
method Uses variational autoencoders and multi-layer perceptrons to encode gene expression and drug data.
result High average R2 of 0.83 and 0.845 in predicting drug responses for breast and pan-cancer cell lines, respectively. 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 model clusters diverse 'omics data for disease subtyping.
problem Clustering diverse 'omics datasets conflates multiple structures.
method Multi-view Bayesian mixture model with semi-supervised learning.
result Identifies distinct clusters of patients for stratified medicine.
EAGLE-Net enhances foundation models by integrating patch-level features for better tissue understanding.
problem Foundation models lack mechanisms for global tissue structure and local context in computational pathology.
method EAGLE-Net combines multi-scale spatial encoding, attention-guided loss functions, and background suppression to aggregate patch-level features into slide-level predictions.
result EAGLE-Net improves classification accuracy and concordance indices across multiple cancer types, producing biologically coherent attention maps.
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.
Flatsomatic compresses cancer mutation data with VAEs, maintaining predictive power.
problem Compressing somatic mutation profiles in cancer while preserving predictive power.
method Flatsomatic uses a Variational Auto Encoder (VAE) with MLP architecture, optimizing evidence lower bound and beta-VAE for latent space regularization.
result Flatsomatic embeddings maintain predictive power of original data, reducing dimensionality from 8,298 to 64.