Genetics

Generative design of sequence specific DNA binding proteins

De novo protein design has advanced rapidly in recent years, yet the programmable recognition of specific DNA sequences remains a longstanding challenge. Here we describe a deep learning based approach for designing sequence selective DNA binding proteins. Our method combines structure generation using RFdiffusion3 with explicit screening against off-target interactions using AlphaFold3. We test t...

UshEffect-3D: Structure-informed Classification of USH2A Missense Variants for Inherited Retinal Disease

Variants of uncertain significance (VUS) in USH2A represent a critical interpretive challenge in inherited retinal disease, with over 70% of ClinVar submissions for this gene currently unresolved. We developed UshEffect-3D, a gene-specific, structure-informed machine learning framework for USH2A missense variant classification. A dataset of 545 curated variants was assembled from ClinVar and LOVD,...

HyperMap: An Efficient Framework for Transferring Perturbation Responses Across Diverse Biological Contexts

Recent perturbation atlases profile transcriptional responses to thousands of targeted perturbations in a reference cell type. Generalising these data...

PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction

Cancer survival prediction from multi-omics data remains challenging because prognostic signals are high-dimensional, heterogeneous, and distributed a...

MIMIC: A Generative Multimodal Foundation Model for Biomolecules

Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation mode...

Aycromo: An Open-Source Platform for Automatic Chromosome Detection in Metaphase Images Based on Deep Learning

Chromosome analysis is a fundamental step in the diagnosis of genetic diseases, but the manual karyotyping workflow is time-consuming and heavily depe...

A machine learning approach to infer DNase1L3 activity from plasma cell-free DNA fragmentomics

DNase1L3 is an endonuclease that fragments DNA during apoptosis and digests DNA from microparticles in plasma, shaping key features of cell-free DNA (...

A Modular Framework for Automated Segmentation and Analysis of AFM Imaging of Chromatin Organization

Chromatin organization underlies essential genome functions, but its nanoscale organization remains challenging to capture and quantify with precision...

GlioVision: A Multi-Modal MRI Framework for Non-Invasive Glioma Molecular Biomarkers Prediction

Gliomas are aggressive primary brain tumors that necessitate critical molecular biomarker predictions for optimal clinical decision-making. Traditiona...

Multi-Modal Deep Learning Integrates Spatial Topologies and Sequential Motifs to Identify Class I HDAC Inhibitors as Pan-Cancer Therapeutics

The molecular characterization of human solid growths has introduced immense genomic complexity and intra-tumoral diversification. Converting these de...

Housekeeping Gene Expression Normalization in Transcriptomics Mitigates Data Leakage in Machine Learning Models

Background: Inappropriate normalization can lead to data leakage and overfitting in machine learning models. Accurately identifying housekeeping genes...

GenNA: Conditional generation of nucleotide sequences guided by natural-language annotations

Deciphering the mapping between linear biomolecular sequences and complex biological functions remains a central challenge in genomics. Although exist...

SNPic: SNP Topic Modeling for Interpretable Clustering of Complex phenotypes

Genome-wide association studies (GWAS) have cataloged thousands of disease-associated variants, yet a central challenge remains: decoding the shared, ...

CellPulse: A Foundation Model of Coordinated Gene Dynamics Simulating Viral Infectious Diseases

Understanding how cells respond to perturbations like viral infections requires models capturing coordinated gene dynamics. However, current gene expr...

Turep: Detecting cross-cancer tumor-reactive T cells in single-cell and spatial transcriptomics data

Tumor-infiltrating lymphocytes are essential for anti-tumor immunity, yet distinguishing tumor-reactive T cells from non-reactive bystander cells rema...

Aiki-XP: leakage-controlled multimodal prediction of within-species relative protein expression at pan-bacterial scale

Generalizable protein-expression prediction can accelerate protein engineering, inform disease mechanisms, and help optimize heterologous recombinant ...

Cross-Attention Over RNA And Protein Sequences Enables Generalizable Interaction Prediction

Computational predictions are essential to characterize the RNA-protein interaction landscape, yet a persistent gap between benchmark performance and ...

OneGenomeRice (OGR): A Genomic Foundation Model for Rice

The transition of genomics to a predictive intelligence discipline is driven by the advent of genomic foundation models. While substantial progress ha...

Back to BERT in 2026: ModernGENA as a Strong, Efficient Baseline for DNA Foundation Models

Recent advances in DNA language models have mainly come from building larger and more complex architectures, making it harder to understand the effect...

Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry

Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, exhibits profound histological and molecular heterogeneity. While g...