deep learning

DisCell: Separating Spillover from Niche Response in Spatial Transcriptomics

DisCell identifies and separates spillover from niche responses in spatial transcriptomics data, confirming a previously reported finding about esophageal cancer cells.

Multimodal cellular context enables molecular phenotyping from single-molecule trajectories

The paper introduces MIST, a framework that integrates single-molecule trajectory data with cellular context to improve molecular phenotyping in E. coli.

SOPSim: A cell vertex-based application for simulating sensory bristle patterning with dynamic filopodia

This study introduces SOPSim, a simulation tool that models sensory bristle patterning in fruit flies by incorporating filopodia dynamics and Notch signaling.

scCont: Interpretable Contrastive Learning for Functional Characterization of Cellular State Transitions

scCont is an unsupervised contrastive learning framework that extracts interpretable functional transition units from scRNA-seq data, outperforming baseline methods in capturing biologically meaningful dynamics.

Rapid, accurate, and memory-frugal sub-gene-level quantification of probe-based, multiplexed single-cell data

This paper introduces simpleaf, a tool for efficient sub-gene-level quantification of probe-based single-cell data, showing improved speed and memory use compared to existing tools.

Automahout: an autonomous image analysis system for cell tracking

Automahout autonomously tracks cells in time-lapse images using a self-tuning system that annotates, trains, and refines models without human input, achieving high TRA scores on public datasets.

Clinical relevance of diffusion tensor imaging-based parameters estimated with deep learning: the effect of model retraining

This study evaluates how retraining deep learning models affects the statistical consistency of diffusion tensor imaging parameters in bipolar disorder, showing significant variability across models.

RadMultiBench: A Reproducible Dual-Task Benchmark Exposing the Limits of Pre-Trained Vision-Language Models Across Thoracic and Breast Imaging

RadMultiBench evaluates pre-trained vision-language models on medical imaging tasks, showing performance gaps in report generation and classification across thoracic and breast imaging.

Towards an analytical framework for investigating the neurophysiological influence of social context in speech perception

The paper introduces a framework combining a fine-tuned LLM and TRFs to isolate social context's influence on speech perception, showing social context improves EEG prediction.

Variation in the embryonic cell lineages of Caenorhabditis elegans natural populations

The paper quantifies developmental variation in C. elegans natural populations using embGAN, finding genotype-specific differences in cell-cycle timing that exceed within-strain variation.

SPECHTacle: An Efficient Detection and Visualization of Cell Specific Regulation in Single Cell-Based Experiments Using a Novel Combinatorial Computational Tool

SPECHTacle efficiently identifies and visualizes cell-specific regulatory changes in single-cell experiments using optimal transport and entropy analysis.

Classifying tumors from broad category to hierarchical molecular subtype: an interpretable hierarchical framework for precision oncology

This paper introduces HHRM, a hierarchical model for tumor subtype classification, achieving high accuracy in pan-cancer and breast cancer subtyping with interpretable biomarker identification.

Long-read shotgun metagenomics identifies distinct urogenital microbial community states associated with functional potential and urinary tract infection during pregnancy

This study uses long-read shotgun metagenomics to identify distinct urogenital microbial community states in pregnant women, linking them to functional potential and UTI risk.

Earl Grey ParTEA: scalable, consistent transposable element annotation from pangenomes to deep phylogenetic scopes

Earl Grey ParTEA annotates transposable elements across multiple genomes, revealing shared and private TE families and improving annotation consistency.

From smartphone to gene: Automated image-based phenotyping captures flowering diversity of common buckwheat

The paper uses smartphone imaging and automated analysis to phenotype flowering traits in buckwheat, identifying heritable traits and genomic regions associated with flowering patterns.

Inferring modularity in genotype-phenotype maps using patterns of epistasis and pleiotropy

This paper introduces two methods, SLED and GTD, to infer modular structure in genotype-phenotype maps by combining epistasis and pleiotropy, showing they yield similar modular architectures in yeast data.

Unified cell segmentation for spatial transcriptomics via transcript reassignment

Tracel improves cell segmentation in spatial transcriptomics by assigning transcripts to cells using optimal transport, enhancing cell-type separation and reducing contamination.

brisc: a multithreaded CPU toolkit for single-cell analysis

brisc is a multithreaded CPU toolkit for single-cell analysis that is faster and more reproducible than existing tools like Scanpy and Seurat.

SQANTI-evidence: isoform-resolved, evidence-tiered structural annotation of unannotated genomes from long-read RNA-seq

SQANTI-evidence annotates genomes from long-read RNA-seq without a reference, achieving high precision in gene and isoform prediction across species.

NORMA 3.0: aesthetic and group-aware visualization and analysis of annotated biological networks

NORMA 3.0 provides enhanced visualization and analysis of annotated biological networks with improved group representation and layout algorithms.

Uncovering cross-signal interactions in single-cell and spatial omics

CRISP identifies and localizes interactions between biological signals in single-cell and spatial transcriptomic data, revealing their effects on tissue function and gene regulation.

Sourmash accurately profiles strains from long-read metagenomes preserving gene content

This study evaluates Sourmash's ability to profile microbial strains from long-read metagenomes, showing accurate gene content recovery and abundance estimation.

Neural dynamics become significantly faster and more nonlinear at the peak of information processing

The paper shows that neural populations adjust their optimal temporal resolution during stimulus processing, becoming faster and more nonlinear when decoding information is most accurate.

Layer-Specific Source-Anchored Transfer Learning for Multi-Omics Survival Prediction in Younger Breast Cancer Patients

This paper introduces LSAT-EN, a transfer learning method that improves survival prediction for younger breast cancer patients using multi-omics data from older patients.

Discovering compositional, interpretable structure in social behavior

This paper introduces a method to model social behavior using decomposed linear dynamical systems, showing it captures complex, compositional actions more effectively than existing methods.

Leveraging short and long-read sequencing to comprehensively characterize alternatively polyadenylated and alternatively spliced transcripts

The paper uses short and long-read sequencing to identify alternatively spliced and polyadenylated transcripts in mantle cell lymphoma, revealing novel splice variants and polyadenylation signals.

2D U-NET Deep Learning Architecture for Atherosclerotic Lesion Segmentation and Quantification

This paper develops a 2D U-NET model to segment and quantify atherosclerotic lesions in mouse aortic sections, achieving high accuracy and significantly reducing measurement time.

Deep Recovery of Effective Migration Surfaces

This paper introduces dreems, a graph neural network that efficiently estimates effective migration surfaces from genetic data, achieving lower error and faster computation than existing methods.

Discovering Prognostic Risk Strata and Heterogeneous Progression Routes from Cross-Sectional Survival Data

GOTSurv models patient-specific disease progression from cross-sectional survival data, identifying heterogeneous risk strata and individualized progression trajectories without predefined risk cut-offs.

Thalamic stereo-EEG detects sleep stages N2 and N3 with discrimination approaching scalp EEG

This study shows thalamic stereo-EEG can detect sleep stages N2 and N3 with high accuracy, comparable to scalp EEG, using machine learning classifiers.