variational autoencoder

LGTM: Gaussian Process Modulated Neural Topic Modeling for Longitudinal Microbiome

LGTM couples a neural topic model with additive basis-function Gaussian processes, learning microbial co-abundance topics and how covariates relate to their trajectories in longitudinal gut microbiome data.

QBayMic: Quantum-coupled variational Bayes for clustering and feature selection in low-signal microbiome data

QBayMic improves clustering and feature selection in low-signal microbiome data by using quantum-coupled variational Bayes, outperforming classical methods in stability and accuracy.

Setting the SCENE for Interpretable Cell-Gene Embeddings in Single-Cell RNA-seq

This paper introduces SCENE, a probabilistic model for embedding cells and genes in scRNA-seq data, achieving state-of-the-art performance and interpretable visualization.

Discovery of microbial intergenic features with genomic language modeling and multimodal search

The paper identifies microbial intergenic features using a sparse autoencoder and multimodal search, revealing previously uncharacterized regulatory DNA and structured RNA elements.

Improved Stability and Interpretability of Motor Modules Computed with an Autoencoder

This paper introduces an autoencoder-based method for computing motor modules from EMG data, showing improved stability and biomechanical interpretability compared to non-negative matrix factorization.

Modelling interpretable patient-level representationsfrom structured and simple multimodal data

The paper introduces FACTMx, a variational framework that jointly models structured and simple patient-level data to learn interpretable representations, showing improved performance in simulations and applications to cancer and cardiovascular cohorts.

An Integrated Single-Nucleus Atlas Resolves Cell-Type-Specific Programs and Molecular Subtypes in Alzheimer's Disease

The paper constructs a single-nucleus transcriptomic atlas of Alzheimer's disease and identifies cell-type-specific molecular subtypes and therapeutic targets using a new framework.

scDisent: regulatory-aware disentangled representation learning for multi-omic single-cell analysis

scDisent separates regulatory and expression variables in multi-omic single-cell data, improving integration and interpretability.

MOFTy: Multimodal Gaussian Process Factor Analysis with Numerical Information Field Theory

MOFTy disentangles spatial structures and recovers cross-modal patterns in multi-omics data by separating smooth gradients from fine-scale heterogeneity.

Learning with Recurrence Geometric AI in Spatial Transcriptomics

This paper introduces RGAI, a geometric deep-learning framework for cross-domain analysis of spatial transcriptomics, enabling alignment of latent tissue states across diverse datasets.

UDIST: unsupervised disentanglement of shape and texture for multi-scale phenotypic profiling in 2D microscopy

UDIST separates shape and texture in microscopy images into independent latent subspaces, enabling unbiased phenotypic analysis across biological scales.

Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer

The paper introduces STORM, a method to identify recurrent spatial motifs in colorectal cancer without cell-type labels, finding a fibroblast barrier linked to immune exclusion and prognosis.

Dynamics of ML-based Morphological Features Indicate a Shear Stress-Dependent Bifurcation of hiPSC-Derived Endothelial Cell States

The paper uses ML to analyze morphological dynamics of hiPSC-derived endothelial cells under shear stress, identifying a bifurcation between two stable cell states.

OMIDIENT: Multiomics Integration for Cancer by Dirichlet Auto-Encoder Networks

OMIDIENT is a multi-omics integration method that uses Dirichlet auto-encoders to improve clustering, classification, and data reconstruction across cancer types.

ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics

ARCADIA integrates scRNA-seq and spatial proteomics to reveal spatially dependent transcriptional programs without cell barcode pairing or feature-level correspondence.

Temporally overlapping mechanisms diversify clonal B cell responses in vivo.

Single-cell RNA and BCR sequencing of infected mouse spleens shows class switching overlaps clonal expansion, GC-seeding clones also form plasmablasts, and SHM proceeds at constant rate.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.