Nov 10, 2022 · This paper introduces an end-to-end methodology to predict a pathway-related outcome and identifying predictive factors using autoencoders. A formal description of autoencoders for explainable binary predictions is presented, along with two objective functions that allows for filtering and inverting negative examples during training.

Oct 10, 2024 · To obtain a latent space useful for sampling new points between two ensembles and predicting transition pathways, our approach uses an autoencoder to map protein conformations into a lower dimensional latent space in a …

We suggested a multi-modal sparse denoising autoencoder architecture that allows for an effective and interpretable combination of multi-omics data and pathway information. Our specific model addresses the high dimensionality of omics data.

Mar 17, 2025 · Protein–protein interaction (PPI) networks effectively capture the functional context of genes, including pathway and complex activation as well as signal transduction.

Dec 28, 2021 · Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in …

Jun 16, 2023 · In this work, we demonstrate that OntoVAE can be applied in the context of predictive modeling and show its ability to predict the effects of genetic or drug-induced perturbations using different ontologies and both, bulk and single-cell transcriptomic datasets.

Feb 1, 2018 · Pathways encode strong methylation signatures that distinguish biologically distinct subtypes. A novel signature-based alignment method called Deep Encoded Epigenetic Pathway Aligner (DEEPAligner) is proposed to identify conserved methylation patterns across pathways.

We propose a novel configurable prior-knowledge-based deep auto-encoding framework called PAAE and its generative variant PAVAE, for analyzing cancer RNA-seq data. Our method constrains its learned internal representation with biological pathways, providing interpretability without sacrificing predictive power.

Sep 28, 2021 · To provide further biological insights, we introduce a novel sparse Variational Autoencoder architecture, VEGA (VAE Enhanced by Gene Annotations), whose decoder wiring mirrors user-provided gene...

Nov 10, 2022 · This paper introduces an end-to-end methodology to predict a pathway-related outcome and identifying predictive factors using autoencoders. A formal description of autoencoders for explainable binary predictions is presented, along with two objective functions that allows for filtering and inverting negative examples during training.