Genetics

Hybrid deep learning–mechanistic modeling of cellular dynamics from a spatiotemporal single-cell atlas

Single-cell measurement technologies provide a powerful framework for studying cellular heterogeneity, transitions, and regulatory networks, yet reconstructing the underlying dynamical processes governing these transitions remains a major challenge due to the high dimensionality of gene expression data. To address this, we develop a variational autoencoder (VAE)–latent neural ordinary differential...

A blueprint for mutation-defined hallmark vulnerabilities across human cancers

Hallmark gene mutations shape cancer cell vulnerabilities and inform drug discovery1–3. A systematic map of hallmark gene mutation-defined cancer dependencies and therapeutic responses is essential to uncover novel targets and refine therapeutic strategies. Here, we present the first pan-cancer blueprint of hallmark vulnerabilities, systematically linking hallmark gene mutation markers to cancer c...

Multiplex mapping of protein-protein interaction interfaces

We describe peptide mapping through Split Antibiotic Resistance Complementation (SpARC-map), a method to identify the probable interface between two i...

Usefulness of scRNA-seq data in predicting plant metabolic pathway genes

It is an ever challenging task to make genome-wide predictions for plant metabolic pathway genes (MPGs) encoding enzymes that catalyze the biosynthesi...

Consensus Pituitary Atlas, a scalable resource for annotation, novel marker discovery and analyses in pituitary gland research

Previous single-cell profiling studies of the pituitary gland have yielded minimally reproducible insights largely due to their low statistical power ...

Circulating DNA reveals nucleosome occupancy patterns that are associated with nucleosome-DNA affinity and are affected in cancer

The study of cell-free circulating DNA (cirDNA) fragments (fragmentomics) from liquid biopsies has received increasing attention. By constructing an a...

RNA sequence design and protein–DNA specificity prediction with NA-MPNN

RNA sequence design and protein–DNA binding specificity prediction can both be framed as nucleic acid inverse-folding problems: finding the most likel...

Cross-Species Morphology Learning Enables Nucleic Acid-Independent Detection of Live Mutant Blood Cells

In hematology/oncology clinics, molecular diagnostics based on nucleic acid sequencing or hybridization are routinely employed to detect malignancy-as...

Integrative machine learning predicts activating kinase mutations for precision oncology

Kinases are enzymes that catalyze phosphorylation and play crucial roles in a myriad of cellular regulatory processes and hemostasis. Patient-specific...

System-level health profiling from blood DNA methylation with explainable deep learning

Genome-scale DNA methylation (DNAm) profiles capture organismal physiology, but most predictive models lack transparency and multi-level applicability...

Integrating Mutation and Stop Signals for Improved RNA Structure Analysis and Insight Discovery

Understanding RNA structures is essential for exploring its diverse cellular roles. Chemical modification-based RNA structure probing remains a key ap...

Scaling Large Language Models for Next-Generation Single-Cell Analysis

Single-cell RNA sequencing has transformed our understanding of cellular diversity, yet current singlecell foundation models (scFMs) remain limited in...

From sequence to signature: Machine learning uncovers multiscale feature landscapes that predict AMR across ESKAPE pathogens

Since the clinical introduction of antibiotics in the 1940s, antimicrobial resistance (AMR) has become an increasingly dire threat to global public he...

IRIS: A Machine Learning-Based Pose Re-Ranking Tool for RNA-Ligand Docking

RNA-ligand docking remains challenging, due in part to intrinsic properties of RNA such as structural flexibility and a highly charged phosphate backb...

Performance and limitations of out-of-distribution detection for insect DNA (meta)barcoding

Successful applications of DNA barcoding/metabarcoding rely on the accurate taxonomic identification of sequence fragments. When biological surveys wi...

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

In-silico prediction of protein mutant stability, measured by the difference in Gibbs free energy change (ΔΔG), is fundamental for protein engineering...

GREmLN: A Cellular Graph Structure Aware Transcriptomics Foundation Model

The ever-increasing availability of large-scale single-cell profiles presents an opportunity to develop foundation models to capture cell properties a...

Learning the DNA syntax of human microbiomes to infer health and disease

The human microbiome is a key factor in human health and alterations in community structure are associated with diverse pathological conditions. Howev...

Controlling Payload Heterogeneity in Lipid Nanoparticles for RNA-Based Therapeutics

Lipid nanoparticles (LNPs) are a leading platform for nucleic acid delivery, yet conventional assembly by mixing lipids with RNA yields particles with...

Multi-modal data integration for machine learning applications

The integration of multi-modal genomic data, encompassing sequences, annotations, and coverage tracks, remains a major bottleneck in bioinformatics, b...