Latest AI and machine learning research in genetics for healthcare professionals.
Foundation models exhibit strong capabilities for downstream tasks by learning generalized representations through self-supervised pre-training on large datasets. While several foundation models have been developed for single-cell RNA-seq (scRNA-seq) data, there is still a lack of models specifically tailored for single-cell ATAC-seq (scATAC-seq), which measures epigenetic information in individua...
Understanding how microbial communities respond to disturbance remains a fundamental question in ecology, with broad implications for biodiversity, ecosystem function, and biotechnology. Trait-based approaches offer general rules to predict community responses by linking ecological strategies to measurable traits. While life-history strategy frameworks such as the competitor–ruderal–stress-toleran...
DNA methylation is a conserved epigenetic modification essential for maintaining genome stability. However, how methyltransferases maintain CG methyla...
Rising antimicrobial resistance (AMR) in Escherichia coli bloodstream infections (BSIs) in high-income settings has typically been dominated by one cl...
Deep learning models can accurately reconstruct genome-wide epigenetic tracks from the reference genome sequence alone. But it is unclear what predict...
The nasopharyngeal microbiome acts as a dynamic interface between the human body and environmental exposures, modulating immune responses and helping ...
The rapid evolution of SARS-CoV-2 presents significant challenges for modeling viral dynamics, driven by lineage diversification and region-specific m...
Avian influenza remains a serious risk to human health via zoonotic transmission, as well as a feasible pandemic threat. Although limited zoonotic cas...
Repeated pregnancy loss (RPL) is a multifactorial condition in which the underlying immunological mechanisms remain incompletely understood. Although ...
RNA-based therapies are a rapidly expanding field, offering treatments for a wide range of diseases, including many rare conditions. To date, 24 RNA t...
VCFs are the most widely used data format for encoding genetic variation. By design, standard VCFs do not include data from sites where all individual...
Recent advances in Natural Language Processing (NLP) have spurred the application of Large Language Models (LLMs) to bioinformatics, enabling innovati...
Premature termination codons (PTCs) are a major cause of genetic diseases, but the efficacy of therapeutic readthrough agents is highly context-depend...
Multiple positive-sense, single-stranded RNA (+ssRNA) viruses cause human diseases, ranging from mild colds to deadly pandemics. These viruses share a...
Microbiome research has been limited by methodological inconsistencies. Taxonomy-based profiling presents challenges such as data sparsity, variable t...
Genomic Language Models (GLMs) suffer from the inherent problem of data scarcity, due to the cost, time and complexity of wet-lab experiments. Data au...
Genomic language models (gLMs) have emerged as a powerful approach for learning genome-wide functional constraints directly from DNA sequences. Howeve...
In the past decades, a wide suite of design tools for biological systems have been developed, but using these to create biotechnologies that achieve r...
Lysine methylation is a dynamic and reversible post-translational modification of proteins carried out by lysine methyltransferase enzymes. The role o...
Predicting cellular responses to genetic perturbations is critical for advancing our understanding of gene regulation. While single-cell CRISPR pertur...