Latest AI and machine learning research in genetics for healthcare professionals.
Transcriptomics datasets generated using next-generation sequencing techniques such as single cell RNA-sequencing (scRNA-seq) and spatially-resolved transcriptomics (SRT) allow researchers to study patterns in gene expression across celltypes, temporal processes, and spatial organization at ever-higher resolutions and depths. scRNA-seq analyses produce gene expression profiles and celltype-specifi...
DNA encodes biological function across a continuum of sequence scales, from single-nucleotide and motif-level grammar to regulatory neighborhoods, chromatin-scale organization and evolutionary constraint. A useful model of genomes should therefore do more than classify short sequence windows: it should maintain nucleotide-resolution state over long contexts, score counterfactual mutations, conditi...
Genetic prediction of complex phenotypes typically relies on additive linear models, which scale well but cannot capture non-additive effects or deepl...
Standard masked-language-model fine-tuning applies a uniform masking probability across every token position, assuming reconstruction difficulty is po...
Vision-language models (VLMs), such as CLIP, are vulnerable to adversarial attacks, posing a serious problem for real-life applications and deployment...
EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject lab...
Foundation models trained on electronic healthcare records (EHRs) have gained traction with the aim to transform personalised medicine. However, their...
Background Cell free DNA (cfDNA) methylation profiling is promising for minimally invasive cancer detection, but its translation is limited by high di...
Single-cell RNA sequencing has enabled the construction of comprehensive cell atlases, yet the quality and coherence of the cell-type annotations with...
Vision--Language Models (VLMs) are increasingly deployed through a model supply chain in which pretrained checkpoints, architecture definitions, text ...
Adenosine-to-inosine (A-to-I) RNA editing is a widespread post-transcriptional mechanism that diversifies the transcriptome. While ADAR enzymes cataly...
CVDs are heterogeneous, multifactorial disorders that remain the leading cause of global mor- tality from infancy to old age. It requires an early ide...
Interpreting large-scale singlecell transcriptomic data remains a major challenge for understanding disease mechanisms. Recent single-cell foundation ...
Predicting single-cell responses to genetic perturbations could reveal the vast combinatorial space of perturbations and cellular contexts that is inf...
Objective: Genetic disease is common in Level IV Neonatal Intensive Care Units (NICUs), yet clinicians often struggle to identify infants who would be...
The advent of deep learning-driven tools such as AlphaFold has revolutionized the prediction of biomolecular structures, offering unprecedented accura...
Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike...
Autoregressive foundation models for electronic health records (EHRs) typically inherit pretraining methods from language modeling, where patient traj...
Tumor cellular composition, including malignant cell states, immune populations, and stromal populations, is increasingly recognized as a determinant ...
Learned generative priors are increasingly used for ill-posed Bayesian inverse problems, their posterior uncertainty treated as earned from data. But ...