variational autoencoder

GraphBG: Fast Bayesian Domain Detection via Spectral Graph Convolutions for Multi-slice and Multi-modal Spatial Transcriptomics

GraphBG detects spatial domains in multi-slice and multi-modal spatial transcriptomics data using spectral graph convolutions and Bayesian modeling, achieving high accuracy and scalability.

MoCoO: Momentum Contrast ODE-Regularized VAE for Single-Cell Trajectory Inference and Representation Learning

MoCoO improves single-cell trajectory inference by combining VAE, Neural ODE, MoCo, and Flow Matching, achieving top performance in clustering and embedding quality across multiple datasets.

Transcriptional Hysteresis and Irreversibility in Periodontitis Revealed by Single-Cell Latent Manifold Modeling

This study uses single-cell RNA sequencing to reveal transcriptional irreversibility in periodontitis, introducing the Regenerative Permission Index to predict treatment failure.

Integrating single-cell and single-nucleus datasets improves bulk RNA-seq deconvolution

This study improves bulk RNA-seq deconvolution by integrating single-cell and single-nucleus datasets through filtering and transformation strategies, achieving near-scRNA-seq accuracy.

GRNFormer: Accurate Gene Regulatory Network Inference Using Graph Transformer

GRNFormer infers gene regulatory networks from transcriptomics data using a graph transformer framework, outperforming existing methods and recovering known and novel regulatory interactions.

The Risk of Gulf Birds Functional Diversity Loss with Climate Change Uncovered Using Deep Learning Population Models

The paper uses deep learning to model how climate change and sea-level rise affect bird functional diversity in the Gulf of America, finding unexpected declines in upland areas and increases in shoreline diversity.

General-purpose embeddings for long-read metagenomic sequences via β-VAE on multi-scale k-mer frequencies

The paper introduces a β-VAE-based embedding for long-read metagenomic sequences using multi-scale k-mer frequencies, achieving high clustering purity and revealing eukaryotic contigs overlooked by prokaryotic tools.

New-to-nature PHA synthase design using deep learning

This study uses a conditional variational autoencoder to design new-to-nature PHA synthases, which are validated for activity in yeast and show potential for expanding PHA material properties.

Learning Continuous Morphological Trajectories via Latent Principal Curves

The paper introduces MorphCurveVAE, a method to infer continuous morphological trajectories from static 3D microscopy images, demonstrating its effectiveness on mitosis data.

A Unified Dynamical-Systems and Control-Theoretic Model for Single-Cell Fate Dynamics

This paper introduces a unified dynamical-systems and control-theoretic model to predict single-cell fate dynamics by integrating pseudotime, RNA velocity, optimal transport, and stochastic trajectories under partial observability.

Generative design of synthetic gene circuits for functional and evolutionary properties

This paper uses a generative model to design synthetic gene circuits that achieve specific functional and evolutionary properties.

Neretva: Neural Variational Inference for Allele-level Genotyping of Highly Polymorphic Genes

Neretva improves allele-level genotyping of highly polymorphic genes using a probabilistic model and variational inference, achieving competitive accuracy on CYP and KIR families.

PepCABO: Latent-space Bayesian optimization for peptide-MHC binding using contrastive alignment

PepCABO improves peptide-MHC binding optimization using contrastive alignment and Bayesian optimization in a latent space, outperforming baselines across multiple alleles.

Explainable Graph Learning for Multimodal Single-Cell Data Integration

SCPRO-VI integrates multi-omic single-cell data using a variational graph auto-encoder and biologically guided metrics, improving subcluster identification.

HuiduRep: A Robust Self-Supervised Framework for Learning Neural Representations from Extracellular Recordings

HuiduRep is a self-supervised framework that learns robust representations from extracellular recordings, improving spike sorting performance across datasets and outperforming existing tools.

MultiGAI: Global Attention-Based Integration of Single-Cell Multi-Omics with Batch Effects Correction

MultiGAI corrects batch effects in single-cell multi-omics data while preserving biological signals, showing effectiveness in integration and correction tasks.

Generative AI-based design of hybrid transcriptional activator proteins with new DNA-binding specificity

The paper uses a variational autoencoder to generate hybrid transcriptional activators with new DNA-binding specificity, showing functional variants that recognize multiple promoters.

Decoupling Lineage and Intrinsic Information in Single-Cell Lineage Tracing Data with Deep Disentangled Representation Learning

DeepTracing disentangles lineage and intrinsic cellular variation in single-cell lineage tracing data, outperforming existing methods in clustering, phylogenetic recovery, and tumor-type classification.

Towards building a World Model to simulate perturbation-induced cellular dynamics by AlphaCell

AlphaCell simulates perturbation-induced cellular dynamics by addressing limitations in current models through three innovations: latent manifold rectification, biological reality reconstruction, and universal state transition.

Arborist: Prioritizing Bulk DNA Inferred Tumor Phylogenies via Low-pass Single-cell DNA Sequencing Data

Arborist improves tumor phylogeny reconstruction by integrating bulk DNA-seq and low-pass scDNA-seq data, outperforming existing methods on simulated and biological datasets.

Partially Shared Multi-Modal Embedding Learns Holistic Representation of Cell State

APOLLO learns shared and modality-specific information from multi-modal single-cell data to improve understanding of cell state and regulatory mechanisms.

ABaCo: Addressing Heterogeneity Challenges in Metagenomic Data Integration with Adversarial Generative Models

ABaCo integrates metagenomic data from multiple studies, corrects technical heterogeneity, and outperforms existing methods while preserving biological signals.

Skip-Zeros Variational Inference in the Million-Cell Era of Single-Cell Transcriptomics

UNISON is a scalable framework for single-cell transcriptomics that uses skip-zeros variational inference to efficiently analyze large, sparse datasets while preserving interpretability.

Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid Intelligence

The paper introduces a conditional graph variational autoencoder to model brain functional connectivity, reconstructing network patterns conditioned on sex and fluid intelligence while preserving interpretable latent features.

LLM-Evolved Regularization Schedules Prevent Posterior Collapse in Latent Factor Analysis via Dynamical Systems

The paper evolves regularization schedules using LLMs to prevent posterior collapse in LFADS without population-based training, improving KL divergence and stability.

A hyperparameter benchmark of VAE-based methods for scRNA-seq batch integration

This study benchmarks VAE-based methods for scRNA-seq batch integration, finding dataset-dependent performance trade-offs and recommending hyperparameter settings for optimal results.

Latent-centric Isotropic Resolution Enhancement for Expansion Microscopy Imaging via Neural Compression and Self-supervised Learning

This paper introduces a self-supervised framework for isotropic super-resolution and compression in expansion microscopy, enabling efficient storage and analysis of whole-organ datasets.

Integration of Unpaired and Heterogeneous Clinical Flow Cytometry Data

This paper introduces UVAE to integrate unpaired clinical flow cytometry data, improving cell type signal and clustering by correcting batch effects.

Mapping trans-eQTLs at single-cell resolution using Latent Interaction Variational Inference.

This paper introduces LIVI, a deep learning framework for mapping trans-eQTLs at single-cell resolution, and shows it detects more trans-eQTLs than existing methods and identifies cell types and genes involved in autoimmune disease risk.

Learning Genetic Perturbation Effects with Variational Causal Inference

This paper introduces SCCVAE, a hybrid model combining mechanistic causal models with deep learning to predict and simulate genetic perturbation responses in single-cell data.