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The paper introduces scPFA, a lineage-informed factor analysis method, to uncover heritable gene expression programs from single-cell lineage-tracing data.

ORBIT integrates omics data across studies and layers to identify convergent disease mechanisms by prioritizing concordant signals using rank-based methods.

MAJEC improves TE quantification by jointly estimating gene, isoform, and locus-level TE counts from RNA-seq, reducing exon-overlap contamination and increasing accuracy.

This study identifies potential inhibitors of the Mycobacterium tuberculosis Ag85C enzyme using docking, pharmacophore modeling, and ADME analysis, highlighting three promising compounds with favorable drug-like properties.

HMVA-DDI predicts drug-drug interactions and their severity using a two-stage deep learning framework with improved attention mechanisms and task-specific features.

This study evaluates how pretrained gene representations contribute to virtual spatial transcriptomics by separating their effects on mean expression and spatial patterns across multiple datasets.

The paper introduces a latent Gaussian dynamic graphical model for longitudinal non-Gaussian data, showing it can recover both contemporaneous and lag-one temporal dependence structures in microbiome data.

This paper introduces two tools for improving the trustworthiness of single-cell dimensionality reduction by enhancing quality metrics and multi-scale structure preservation.

The paper analyzes how viral infections affect microbial transcript profiles across insect species using meta-transcriptomic data, revealing host-specific and conserved patterns of virus-microbiome interactions.

This paper introduces AgingHallmarksDB, a resource for systems-level analysis of aging hallmarks and associated genes, demonstrating its utility through various analyses of disease and transcriptome data.

GERO improves enzyme-reaction retrieval and catalytic optima prediction by integrating multimodal data for condition-aware recommendations.

SaVanache provides a visualization tool for exploring pangenome variation graphs, enabling interactive analysis of genetic diversity and structural variations.

The paper benchmarks methods for inferring transcription factor binding specificities using a large community challenge and finds deep learning models outperform traditional methods, though positional weight matrices are competitive.

Deep Lineage uses deep learning to analyze time-series single-cell RNA-sequencing with lineage-tracing data to predict cell fate and gene expression profiles more accurately than existing methods.

scXMatch quantifies distribution shifts between single-cell data from different conditions using a non-parametric graph-based test, showing robust performance across gene expression, chromatin accessibility, and morphology data.

The paper introduces an R package that classifies occurrence records into 31 global biome schemes and ranks them based on data fit.

COSMIC, a generative model, quantifies the relationship between nuclear morphology and gene expression using paired single-cell data, revealing morphology-associated genes in prostate cancer and spatial transcriptomics of zebrafish embryos.

EsViritu is a viral pathogen profiler that accurately detects and reconstructs virus genomes from metagenomes with high sensitivity and specificity.

The paper describes how to construct heatmaps for cell frequency and marker intensity using Excel, Phantasus, or R to improve interpretation of high-content cytometry data.