Latest AI and machine learning research in genetics for healthcare professionals.
Breast cancer subtyping is essential for precision oncology, influencing prognosis, treatment selection, and clinical trial design. The Integrative Subtype Classification (IC) categorizes breast tumors into groups with distinct long-term outcomes based on genomic and correlated transcriptomic features. This method relies on sequencing data, which, despite decreasing costs, is not always available ...
Byte Pair Encoding (BPE) is widely used in genome foundation models for its ability to compress long DNA sequences into fewer tokens. However, its variable-length tokens often span multiple nucleotides, limiting the model’s sensitivity to single-nucleotide variations—an essential requirement for accurate Variant Effect Prediction (VEP). We introduce Mut-BPE, a training-free, plug-and-play tokeniza...
Enhancers control tissue-specific gene expression across metazoans. Although deep learning has enabled enhancer prediction and design in mammalian cel...
A comprehensive all-by-all receptor ligand affinity screen using Boltz-2, a deep learning framework for protein-ligand interaction prediction, reveals...
The design of RNA-guided nucleases with properties not limited by evolution can expand programmable genome editing capabilities. However, generating d...
Adenosine deaminase acting on RNA (ADAR) converts adenosine to inosine within double-stranded RNA (dsRNA) and can be co-opted for therapeutic RNA edit...
Accurate overall survival (OS) prediction is key for personalized treatment in breast cancer, but mutation burden alone is insufficient. To improve pr...
T-cell acute lymphoblastic leukemia is a biologically heterogeneous malignancy characterized by diverse transcriptional and genomic alterations. Recen...
Cardiovascular disease (CVD) remains the leading cause of global mortality, with myocardial fibrosis characterized by excessive extracellular matrix (...
Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity, but its rich, complementary structure acros...
Accurate selection of favourable crop genotypes has motivated the exploration of diverse prediction algorithms for crop breeding applications. One gen...
Craniofacial malformation (CFM) is a congenital defect encompassing a wide range of phenotypic presentations and is largely driven by genetics. Despit...
Multimodal neural networks integrating protein language models (PLMs) with structure-derived features are increasingly common for predicting mutation ...
Single-cell RNA sequencing (scRNA-seq) enables characterization of cellular heterogeneity but lacks spatial context, while Spatially Transcriptomics m...
Deciphering the connectome, anatomy, transcriptome and spatial-omics integrated multi-modal brain atlas and the underlying organization principles rem...
Single-cell RNA sequencing technologies have revolutionized our understanding of cellular heterogeneity, yet computational methods often struggle to b...
Adaptive introgression (AI), the beneficial genetic transfer between species, is key to adaptation, yet its genomic identification is challenging. Exi...
Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of Alzheimer’s disease and aging by revealing cellular heterogenei...
EGFR amplification occurs in approximately 40-50% of glioblastoma (GBM) cases and is critical for treatment selection [1]. However, GBM tissue samples...
Small interfering RNA (siRNA) therapeutics have extraordinary potential for targeted gene silencing. They mediate post-transcriptional gene regulation...