variational autoencoder

Cellpin enables reference-based imputation and denoising of spatial transcriptomes

Cellpin imputes and denoises spatial transcriptomes using a variational autoencoder trained on single-cell RNA-seq, outperforming existing methods without requiring cross-modality alignment.

Praxis-BGM: Clustering of Omics Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference

The hippocampus as a small-world cognitive map

The paper uses a geometry-aware autoencoder to analyze hippocampal activity in mice, revealing a small-world network structure that enables both local fidelity and global searchability in cognitive maps.

In Silico Generation of Gene Expression profiles usingDiffusion Models

This paper introduces diffusion models for generating synthetic gene expression data and shows they outperform existing methods in predictive performance.

bMAE: Masked Autoencoder Latent Representations for Bulk RNA-seq Tissues

The paper introduces a masked autoencoder for bulk RNA-seq data to learn latent representations that outperform existing methods in tissue discrimination and structure preservation.

Scalable multimodal mapping of macrophage regulatory architecture by integrating optical and transcriptomic pooled screens

The paper introduces PerturbPair, a platform integrating transcriptomic and optical screens to map macrophage regulatory networks, revealing cross-modal relationships and disease-relevant gene programs.

A Conditional Variational Autoencoder with QSAR-Guided Surrogate-Weighted Fine-Tuning and Cross-Entropy Optimization for Targeted Antimicrobial Peptide Generation

This paper introduces a conditional variational autoencoder for antimicrobial peptide generation, combining QSAR data and cross-entropy optimization to overcome limitations in generative models.

On Complexity in Resource Constrained Neuronal Systems: Dynamic Resource Theory

This paper introduces Dynamic Resource Theory to explain self-organization in resource-constrained systems, showing how complexity and adaptability emerge through thermodynamic and variational principles.

petVAE: A Data-Driven Model for Identifying Amyloid PET Subgroups Across the Alzheimer's Disease Continuum

petVAE identifies biologically meaningful subgroups along the Alzheimer's disease continuum using a deep learning model trained on amyloid PET scans.

Granger Sensori-Behavioral Taxonomy of Neuronal Ensemble Activity from Two-Photon Calcium Imaging Data

This paper introduces a framework to extract directional functional interactions between neurons, stimuli, and behavior from two-photon calcium imaging data, revealing distinct sensori-behavioral roles in the mouse auditory cortex.

Deep Computational Anatomy via Latent-Aligned Multiview Normalizing Flows

The paper introduces LAMNr flows for multimodal data analysis, enabling shared latent subspaces and cross-view imputation, with applications in biological imaging.

SRSA-VAE: Self-Attention-Based Feature Learning for Single-Cell Multimodal Clustering

SRSA-VAE improves single-cell clustering by using self-attention and residual connections, outperforming existing methods on scRNA-seq and CITE-seq data.

Generative models for antimicrobial peptide design: auto-encoders and beyond

This study compares deep learning models for generating antimicrobial peptides, finding that auto-encoders, especially Wasserstein auto-encoders, produce more diverse sequences than other models.

A variational method for efficient estimation of diffusion and free-energy profiles along collective variables

This paper introduces a variational method to estimate diffusion coefficients and free-energy profiles from molecular dynamics trajectories under equilibrium and nonequilibrium conditions.

Uncertainty-aware localization microscopy by variational diffusion

This paper introduces a variational diffusion model for uncertainty-aware localization in single-molecule microscopy, enabling high-fidelity super-resolution and uncertainty estimation.

DOMINO: Learning Domain Co-occurrence for Multidomain Protein Design

DOMINO generates multidomain proteins by learning domain co-occurrence patterns and using them to design novel sequences with high structural confidence.

BundleWarp: Enhancing White Matter Tractometry and Morphometry with Precise Neuronal Mapping using Streamline-based Nonlinear Registration

BundleWarp improves white matter tractometry by aligning tracts while preserving anatomical features, enhancing sensitivity to disease-related changes in conditions like Alzheimer's and Parkinson's.

Binning meets taxonomy: TaxVAMB improves metagenome binning using bi-modal variational autoencoder

TaxVAMB improves metagenome binning by combining tetranucleotide frequencies and co-abundances with taxonomic annotations, outperforming existing methods on benchmark and real datasets.

Translating Brain Anatomy and Disease from Mouse to Human in Latent Gene Expression Space

This paper develops a VAE-based framework to translate mouse brain gene expression to human, showing that mouse disease models predict human Alzheimer's and Parkinson's patterns and identify the best mouse model for AD patients.

Geometry aware graph attention networks to explain single-cell chromatin state and gene expression

SEAGALL uses geometry-aware graph attention networks to extract biologically relevant features from single-cell data, revealing cell identity drivers beyond marker genes.

Decoding Anadara shell morphology with deep learning

This paper introduces Morpho-VAE, a deep learning framework for analyzing shell morphology, which outperforms traditional methods in species classification and reveals morphological patterns in Anadara shells.

An integrated single cell and spatial omics atlas of human prenatal development

Human Developmental Cell Atlas integrates ~4.6 million cells/nuclei from 4-22 post-conception weeks into 454 refined cell types and maps 114 spatial niches across 6PCW whole-embryo sections.

Canalized gene regulatory networks stabilize floral polymorphism and enable modular transgressive expression

The paper investigates how canalized gene regulatory networks maintain floral polymorphism in Stellera chamaejasme by analyzing transcriptomic and regulatory variation across morphs.

Decoding Single-Cell Omics of Perturbation Responses Using DeSCOPE

DeSCOPE is a variational autoencoder framework for predicting genetic perturbation responses across multiple omics modalities, outperforming baselines in unseen gene and cell type scenarios.

Contrasting population structures coexist in a strain-resolved estuarine microbiome

The paper uses high-resolution metagenomic sequencing to reveal contrasting population structures in a microbial community, showing distinct evolutionary strategies coexisting within a single sample.

Revisiting Reconstruction Likelihood: Variational Autoencoders for Biological and Biomedical Data Clustering

This paper shows that variational autoencoders can effectively cluster data by encoding cluster assignments in their latent space, using reconstruction likelihood to identify typical samples and anomalies.

Interpretable deep generative ensemble learning for single-cell omics with Hydra

Hydra is a deep generative framework for single-cell omics data that improves cell type annotation and feature selection, particularly for rare populations.

CoPhaser: generic modeling of biological cycles in scRNA-seq with context-dependent periodic manifolds

CoPhaser models biological cycles in scRNA-seq by decomposing data into periodic and non-periodic sources, revealing insights into cell cycle dynamics and other periodic processes across diverse contexts.

A hierarchical generative model reveals enhanced latent precision of brain-body interaction dynamics during interoceptive attention

The paper uses a hierarchical generative model to analyze brain-body interaction dynamics during interoceptive attention, finding increased latent precision in multimodal physiological signals.

Simplex-Constrained Neural Topic VAEs with Flow Refinement for Interpretable Single-Cell Gene-Program Discovery

This paper introduces Topic-FM, a neural topic VAE for single-cell transcriptomics that improves interpretability and clustering performance across multiple datasets.