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RNA-SpecFlow: RNA-Conditioned Spectral Flow for Histology Synthesis

RNA-SpecFlow synthesizes histology images conditioned on RNA data, showing that increased detection counts can coincide with reduced segmentation agreement.

Integrative Single-Cell Transcriptomics and Explainable AI for Disease Mechanism and Biomarker Discovery in Triple-Negative Breast Cancer

The paper uses single-cell RNA-seq and explainable AI to identify transcriptional features distinguishing invasive from non-invasive TNBC cells in a cell line, finding EMT-related and inflammatory genes as key predictors.

3D Reconstruction of Nanoparticle Distribution in Tumor Spheroids with Volume Electron Microscopy

The paper presents an end-to-end pipeline for segmenting nanoparticles and cellular structures in 3D electron microscopy data of tumor spheroids, revealing preferential NP clustering near nuclei.

CATFISHR: integration of transcriptome and inferred CNV profiles for comprehensive malignant cell identification

CATFISHR integrates transcriptomic and copy-number data to improve malignant cell identification in scRNA-seq, achieving high accuracy across multiple datasets and identifying previously missed malignant populations.

VenusGenome: Genome-Aware Masked Autoencoding for Microbial Genome Representation Learning

VenusGenome improves microbial genome representation learning by using a genome-aware masked autoencoder, achieving high performance in phenotype prediction and gene-level tasks.

Integrative Biophysical Illumination of the 3D GPCRome Dynamics

The paper develops GPCRmd-NMR, an integrative platform combining MD simulations and NMR spectroscopy to analyze GPCR dynamics at unprecedented scale.

Cellular context defines brain-specific functions of genes associated with neurodevelopmental disorders

The paper identifies brain-specific functions of NDD-associated genes by analyzing gene expression and regulatory mechanisms across tissues.

Mitigating Bias in Spatial Transcriptomic Pipelines via Human Feedback

The paper introduces CSDE, a method that improves differential expression analysis in spatial transcriptomics by incorporating expert feedback to mitigate preprocessing biases.

ArthroVerse: mapping protein family diversity across arthropod-associated microbiomes

ArthroVerse compiles and annotates protein families from arthropod-associated microbiomes to explore functional diversity and host-microbe interactions.

Building virtual cell dynamics that evolve with biological measurements

CellCompass, an agentic AI system, models cell dynamics from single-cell measurements and improves predictions by integrating new biological constraints and experimental data.

How Much Can AI Virtual Cells Gain from Unified Perturbational Modeling?

This paper evaluates how unified perturbational modeling improves AI virtual cell predictors by integrating heterogeneous Perturb-seq datasets and achieving better performance across benchmarks.

AltereGO: Prediction of Protein Function by Multimodal Fusion of Protein Language Models.

AltereGO fuses protein language models and structure representations to predict molecular functions, achieving better performance than individual models and some existing methods.

Evaluation design shapes estimates of cross-donor generalization in single-cell classification of cell-type

This paper evaluates how different evaluation designs affect cross-donor generalization in single-cell classification, finding that random cell-level splitting overestimates performance compared to donor-held-out evaluation.

Asking the Data: Conversational Interrogation of LC-MS/MS Proteomics Data Sets with an LLM Agent

The paper introduces a conversational framework using an LLM agent to interrogate proteomics data sets, identifying issues in four LC-MS/MS datasets and demonstrating its feasibility in reanalyzing data.

Best Practices for Pre-Processing Large-Scale ATAC-Seq Data: Why Epidemiological Studies Demand Greater Stringency

The paper introduces PIPPA, a pipeline for processing large-scale ATAC-seq data, showing that peak calling performance varies by dataset and recommending parameters for high-confidence chromatin accessibility profiles.

BLOOM: Biologically Informed Flow Matching for Transcriptomic Virtual Embryo Modeling

BLOOM generates developmental cell-expression distributions across time using conditional flow matching, outperforming baselines in gene and cell-expression correlations.

MPFIL: Multi-Level Protein Feature Integration for Predicting Mutation-Induced Protein-Protein Binding Affinity Changes

MPFIL predicts mutation-induced changes in protein-protein binding affinity by integrating structural and sequential features across multiple levels.

Mimic: A Unified Neural Network Architecture for Modelling Genomic Sequences and Module-level Comparison

This paper introduces Mimic, a unified neural network architecture for genomic sequence modeling, showing it outperforms existing models in protein-DNA interactions and enhancer activity.

KmerSignificance Score: A discriminative and biologically-informed framework for viral k-mer prioritization

KmerSignificance Score (KSS) prioritizes viral k-mers by integrating strain-distinguishing capacity, mutation impact, and functional importance, achieving superior classification performance and cross-dataset comparability.

Dimensionality Reduction for Cyclic Compositional Data

clrcycle is a dimensionality reduction method for cyclic data that captures phase and magnitude in biological systems, showing effectiveness in circadian and cell-cycle analysis.