Latest AI and machine learning research in genetics for healthcare professionals.
The rational design of high-specificity binders to peptide–HLA (pHLA) complexes remains a major challenge in personalized immunotherapy, particularly for shared neoantigens with single-point mutations. To address this, we have developed an integrated framework that combines knowledge-based deep learning with physics-based simulation for the design of highly specific pHLA binders. Applied to p53 R1...
Metagenomic binning is crucial for reconstructing microbial genomes from metagenomic sequencing samples. However, existing tools struggle in complex communities where short, low-abundance contigs predominate, thereby limiting the recovery of complete metagenome-assembled genomes (MAGs) and the identification of novel functions. Here, we introduce CompleteBin, a Transformer-based framework that int...
Tumor Mutational Burden (TMB) is a widely used biomarker for selecting cancer patients for immune checkpoint inhibitor (ICI) therapy. However, TMB alo...
DNA fiber assays are powerful tools for investigating replication dynamics at the single-molecule level. However, their application and widespread ado...
Predicting enzyme kinetics directly from sequence remains a central challenge in computational biology, particularly in resolving the effects of mutat...
Transcriptional regulation involves complex interactions with chromatin-associated proteins, but disentangling these mechanistically remains challengi...
Neural transplantation holds the potential to repair damaged neural circuits in neurological diseases. However, it remains unknown how the grafted neu...
The spatial arrangement of cells is fundamental to their function, but single-cell RNA sequencing loses spatial context by dissociating cells from tis...
Epidemiological surveillance of Neisseria gonorrhoeae is hindered by the limitations of existing molecular typing methods, such as NG-MAST and MLST, w...
RNA–RNA interactions (RRIs) are fundamental to gene regulation and RNA processing, yet their molecular determinants remain unclear. In this work, we a...
Neuronal activity shapes brain development and refines synaptic connectivity in part through dynamic changes in gene expression. While activity-regula...
Protein foundation models have advanced rapidly, with most approaches falling into two dominant paradigms. Sequence-only language models (e.g., ESM-2)...
Classical phylogenetics assumes site independence, potentially overlooking epistasis. Protein language models capture dependencies in conserved struct...
Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume th...
Ribonucleic acids (RNAs) are involved in many important biological processes. In particular, non-coding RNAs are crucial regulators of cellular proces...
Deciphering the functionality of the noncoding genome which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes rema...
Microbial production of methylmercury from inorganic mercury in rice paddies poses health risks to consumers of this essential dietary staple. Althoug...
Deep generative models for protein structure and sequence are increasingly used to design proteins with therapeutic and industrial applications, but t...
Single-cell perturbation sequencing technologies (e.g., Perturb-seq, CROP-seq), which integrate CRISPR-based gene editing with single-cell transcripto...
Macromolecular complexes in the genetically minimized bacterium, JCVI-syn3A, support gene expression (RNA polymerase, ribosome, degradosome), metaboli...