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Agentic AI enables cell segmentation-free cell-type inference and cell-type-guided cell segmentation in spatial transcriptomics

This paper introduces SSAF-T, a segmentation-free framework for cell-type inference in spatial transcriptomics that avoids cell segmentation and enables accurate cell-type-guided segmentation.

Label-free flow operators and stability operators from single-cell time courses: what local dynamical operators can and cannot identify

This paper introduces scJDO2, a method to separate score and flow operators from single-cell time courses, evaluating their ability to identify unstable cells and bifurcations in hematopoiesis.

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.

Learning interpretable compositional features associated with continuous covariates

This paper introduces IsoVita, a framework for learning interpretable compositional features from continuous covariates, showing improved detection of temporal isoform variation in human brain RNA-seq data.

TestNet-PA: Sampling-Zero-Aware Inference of Microbial Presence-Absence Networks

TestNet-PA addresses sampling zeros in microbial presence-absence networks by modeling them as structural or sampling zeros, improving FDR control and sensitivity compared to Fisher's exact test.

Organotchi: an open-source tool for the automated segmentation, morphological characterization, and longitudinal tracking of organoids

Organotchi automates segmentation and morphological analysis of neural organoids, quantifying features and tracking changes over time to improve quality assessment and downstream analysis.

TailEnrich enables discovery of dysregulated genes through disease sample enrichment testing

TailEnrich identifies dysregulated genes in patient subsets by testing expression distribution tails, outperforming DESeq2 and edgeR in simulated data and uncovering novel genes in Alzheimer's disease.

Residual barcode-like sequences and barcode-associated artifacts after Dorado and Barbell preprocessing of metagenomic reads

The paper evaluates Dorado and Barbell preprocessing tools for metagenomic reads, finding Barbell reduces barcode-like sequences more effectively while retaining most reads.

Hybrid RY-mers increase aDNA alignment sensitivity

This paper introduces hybrid RY-mers to improve aDNA alignment sensitivity and efficiency, showing higher accuracy and reduced runtime compared to existing methods.

Integrative Multiomics Analysis of the Angiogenesis-EMT Interface in Head and Neck Squamous Cell Carcinoma

This study identifies coordinated angiogenesis-EMT gene programs in OSCC using multi-omic analysis and validates candidate genes through external datasets and protein-level confirmation.

LinearRaccess: linear-time approximation of RNA accessibility without base-pair span limits

LinearRaccess computes RNA accessibility for long windows in linear time without base-pair span limits, achieving high accuracy and efficiency.

Development of a Pan-Proteome Multi-Epitope Vaccine Candidate Against Nipah Virus Using Reverse Vaccinology and Immunoinformatics Approaches

This paper designs a multi-epitope vaccine candidate against Nipah virus using immunoinformatics and reverse vaccinology, identifying VC033_V6_BD_L03 as a lead candidate with high global and regional population coverage.

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.

QRAFT: Scaling coalescent-based species tree inference to one million taxa

QRAFT efficiently infers species trees for up to a million taxa using coalescent-based methods, outperforming existing tools in speed and memory usage.

Hierarchical Temporal Transformer for Cancer Grade Prediction and Cross-Cancer Transfer Learning from Pathology Reports

This paper introduces a hierarchical temporal transformer for cancer grade prediction from pathology reports, showing improved performance across cancer types and using visit timing when identifiable.

Learning Strand-Equivariant Tokenizations of DNA with MirrorDNA

This paper introduces MirrorDNA, a DNA language model with a strand-equivariant tokenizer that improves prediction consistency across DNA strands.

MOCHI enables systematic identification of candidate independent intronic transcriptional signals from short-read RNA-seq data

MOCHI identifies candidate independent intronic transcripts from short-read RNA-seq data, revealing novel transcripts associated with cancer survival.

Hub labeling makes exact distance calculation practical for large human pangenome graphs

The paper introduces a hub labeling method to reduce the space required for distance indexing in large pangenome graphs, improving query support and reducing storage needs.

Generative Models for Novel Enzymatic Reactions: How Far Do They Go?

This paper evaluates generative models for predicting novel enzymatic reactions, finding they can generate correct reactions for 25% of test cases and improve over existing methods.

pLM-bVAE: protein language model-enhanced binary representation improves quantum annealing-based protein sequence design

This paper introduces pLM-bVAE, a binary variational autoencoder enhanced with protein language model representations, showing improved sequence reconstruction and design performance in quantum annealing-based protein sequence design.